相机和图像传感器技术基础-第2部分

这是CVP基本摄像机课程的第二节。所以现在我们将谈论数码相机的概念，从模拟和数字概念和成像开始。因此，从像素的电荷必须首先将电压转换为电容，并且这是通过电容电路或更准确地进行的模数转换器完成。必须测量电压电平并转换为使用模数转换器完成的数字，并且沿着在转换之前可以调整增益和偏移量。因此，模数转换器将表示相机的电压电平或灰度级别作为二进制数。作为人类，我们倾向于使用十比特数学，我们在十进制和数量中思考0 - 1 - 2 - 3 - 4.但是电脑和数字设备这样的电脑和数字设备不能像这样想，并将采取更多的行。计算机在二进制中思考，这意味着一个数字仅用为零或一个，然后在二进制文件中计算或在数学中计算，这表示为基本两个数学。所以在二进制中，那么数字都是 - 你可以想象这就像你的车里的里程表，但是里程表只有0和1。所以它从0到1滚动，当它滚动回零时滚动到一个角色，它将下一个数字滚动到一个，这将保持令人想要的，直到这个再次饱和 - 当他们都转到一个时，它们都转到零时下一位数到一个。 So just think of it like your odometer but there’s nothing but a 0 or a 1 on there and we count in binary. When the camera then makes a digital signal it just simply has to make a bit high or low and string the binary number out in this single serial string. So. Why is this important for imaging? Then. What happens is we tend to think of the gray scale values in the camera. The camera is converting light into a value and the more bits we have in the value than the more gray scales we can have and the more accurately we can measure that light. So the depth of the camera is then called the bit depth and that’s how many bits per pixel were being represented in the image. So we start at the bottom. This is what we would call a one-bit image. That means every pixel has one bit representing it, which can only be a zero or one or black and white. So there is only those two values available. So if I looked at a shaded area than I see only black and white. If we increased to four bits per image that means now instead of using one I have four digital numbers and two to the fourth power means I have 16 gray values. Then I can have 16 values as I go from dark to light. A little more accurately represented, but it still seems somewhat cartoony. An 8-bit image which is the most common on the machine vision – two to the eighth powers is then 56 values and I have 256 gray values and you can see that I have a fairly smooth transition from white to black. We have left this blown up and a little pixelated to make the point for you. But essentially I have enough values to make a transition that is smooth to your eyes. So you would say, “Well why don’t I just want more and why don’t I have 12 – 14 - 20 bits just all the time?” Well there are some high bit considerations. With more bits you get more accurate measurements of light. But with more bits you also have more data to transfer, process, and store. Since the data from the image is going across my interface method – and we will talk about interfaces near the end here - if I transmit 12 bit data instead of 8-bits per pixel then I have 50% more data per pixel. When we talk about megapixel cameras - one megapixel two four eight, all way up to 20 and 30 megapixels nowadays at a higher frame rate - this becomes a lot of data. So you want to manage how your bit depth for what you need with the application to get enough bit depth and resolution but yet manage your data and keep it minimal to manage the data transfer and not create a lot of extra work in the PC for properties you don’t necessarily need. The other consideration is the accuracy of the image. With more bits if I have a 10 or 12 bit image then I have 1000 or 4000 gray scale’s per image. Which means I can more accurately sample the light but it also means I’ve more accurately sampled the noise. It doesn’t necessarily mean there’s more noise but it means that I can see the noise in those last bits. This is good in some applications. Some high-end medical professionals actually use that noise to calculate, and do some noise analysis and remove noise from sequenced images. But in general over digitizing and digitizing the noise portion of the camera is not useful and creates just extra data that is not useful bits. So we see this a lot too. It doesn’t make sense to put a 12 bit output on a camera that uses a small pixel CMOS imager. We talked about the small pixels in section 1 and if you only have a 4000 electronic pixel doesn’t make sense to have 4000 grayscale because in the charge conversion you are not going to see a single photon in the well. So the signal-to-noise ratio of a 4000 pixel small electron imager is very small. In fact it’s probably going 30 or 40 dB to get 8-bits you need at least 48 dB of dynamic range or signal-to-noise ratio and to get 12 bits you need 62 dB. So it does not make sense of it a high bit depth 62 dV type output on a imager that only can make 30 or 40 dB. What are the benefits of digital cameras? Why do we see the whole world going from analog to digital - whether the consumer camcorders, whether it be the machine vision cameras - what’s gone on here? In the early days we talked about in section 1 we talked about the TV formats. Everything camera wise was analog in the early days, because I expected to go home and plug it in the TV or plug it into my VCR. It was a standard format, it was transferable and everything worked together and the whole world was analog. But as we saw as imagers became higher resolution, as we became progressive scan and non-interlaced scanning, we left some of the standard formats and in fact in machine vision we want nonstandard formats. One of the drivers here is just getting our arms back around these nonstandard formats in a way we can handle. But there are actually advantages to digital cameras as well that you don’t see in an analog camera. They start with the configurability. Analog cameras tended to also be configurable by switches. As the world moved on we do have 232 some high-end analog cameras had serial control by a PC by and large analog cameras went back to TV days and probably had buttons and switches on them. Digital cameras because they are digital by nature - they came long after the fact and the product development already had a little brain in there to drive it anyway – so that brain tends to be digital and we can talk directly to it and not only can we talk directly to it in a lot of cases there is extra processing and there are features we can do inside the camera. Here you go - with the analog camera we tended to have - this is a representation analog signal - here’s the sink here’s the gray scale values of the horizontal line of the image - and it would go into a PC for machine vision. In the consumer world you would connect it to your TV or VCR, but of course were concerned about the machine vision world and in a few applications you might be monitoring or something, but in most cases users then have to convert to digital to get in the PC to actually do some analysis of the image and create an output or an action that we expect in machine vision. By doing this this is actually the amount we took the nice flexible analog circuit or output from the camera and ran it into a digitizer. That can create some problems - pixel jitter between the two devices, this has to sample precisely at the clock frequency of this, it uses a phase lock loops to lock onto it but there’s always a little bit of jitter. Noise or EMI suppression go hand-in-hand and an analog signal - why the standard - this is one volt peak to peak signal with 200 and approximately 270 mV of sync. That means that only about 700 mV just over 700 mV represents the entire analog portion of the signal. As we saw again in section 1, the pixel intensity is being converted to an analog blip that is being represented here somewhere on the horizontal line. So what that means is “hey, I’m running down a single cable with this analog signal and I’ve only got 700 mV representing my full scale of my pixel and only one millivolts representing the bottom end of that pixel!” Actually in our analog signal there is a 50 mV offset to compensate some of this but essentially I have the entire signal 700 mV. That means that if I pickup as little as like 10 mV coming down my cable. Those 10 millivolts are a substantial portion of that 700 mV signal and that’s where you see noise or interference. So if you’ve ever seen TV and you got a bad signal antenna or someone started a motor or something noisy around it you see snow on the screen, thats because it’s making spikes in here, and those spikes are being interpreted by the receiving device as a bright pixel or a dark pixel if it’s a dive. And that noise is very inherent on here. A digital camera makes a chain of zeros and ones as we described in the binary counting and transmits them over the interface. This is inherently more immune to noise because the noise must upset these two scale levels and neither depending on the method of transfer either these are higher voltage it has to be several volts before it toggles from a 0 to a 1. Or maybe current driven were the voltage levels low but it takes a lot of current. So even if there is noise makes a little bit of voltage on the line, it can’t really build any charge because there is no current behind that noise being picked up. So therefore I’m transferring zeros and ones. The receiver only has to tell whether the line toggled way up here to a one or way down here to zero to get it right. If there’s noise it has to be so great that it offsets and probably it’s not only the cameras fault if you have that much noise in your environment. So inherently digital is more immune to noise and correspondingly is more immune to EMI and other noise sources. This is very important in factory settings - as a camera manufacturer definitely experienced cases were people have done installations - they run cables to close to hundred horsepower electric motors. I saw case actually at an automotive plant were they literally did run a gigabit Ethernet cable over 100 hp electric motor and were reducing currents in there. Digital is not immune entirely but it is more immune than analog. When we talk about digital camera benefits versus analog as I said analog has standard formats EIA NTSC in the United States, CCR all in Europe. But these are standard formats. I can plug anything together, it works but it is in fact analog and has the disadvantages we talked about including interlaced scanning. Whereas digital - we have different formats and we might have different resolutions – VGA, SVGA, XVGA or as we leave these and go on we see resolutions all the way up through about 30 megapixels nowadays. The frame rate might be different as well, not only the resolution, the aspect ratio whether it’s a one to one, a four to three, wide HDTV which happens to be a 16 to 9 aspect ratio. Those are all varying things that we have to deal with on the capture end, but in the digital world we just deal with those in the interface standards. So we make a standard that takes care of all these and in most cases – and we will talk about the standards – that either the format is predefined or its flexible and the camera says “I have this may pixels horizontally, I have this many vertically.” and the digital device configures itself. The point being, in the digital world we have the communication and the wherewithal and the tools that we can set up for these nonstandard formats, whereas in the analog world and your VCR and other things there is just no way that it fits the format doesn’t, and it doesn’t then you going to get an analog capture card and configure it. So digital allows us the chance to standardize the output and make plug-and-play type interfaces. Analog does have more than one format – I’ve described mostly what we call a cause composite format - which is the single cable that your accustom to either a coaxial cable bring the raw signal from the antenna or the RCA type that goes on the front of the VCR that contains the analog signal. In analog there are other cases. There is S-VHS also called YC video and this means that breaks the that analog signal into two components one for intensity one for the chroma. Or RGB which means there is now three or four cables. There is three if its RGB in the sink on the grain is for singles brought separately and RGB but what that means is there’s a separate channel for red, green, and blue, and that that channels only intensity. So RGB is actually the highest level analog format because the color components are separated each to their component plane and that gives a chance to have less crosstalk in between the colors and composite because there is a chroma burst for the entire signal - its four times the speed of the rest of the information - then that means there is little jitter and crosstalk among those colors and S-VHS or YC workers to cables or somewhere in between. But in digital now there is no crosstalk because we represented it as a number. Each pixel goes out discreetly as a number but they’re different formats. There are ways I can tell the host, “this pixel I just sent you was an RGB pixel and has everything in one pixel (red, green, and blue), it’s a 24-bit component with eight bits of each.” It tells it that “I’m a YUV pixels.” So, I am not going to go into the coding here, but this coding just says “this is how I encoded digital standard YUV 422.” It might be a monochrome saying, “that is a single 8-bit or a single 16-bit value for this pixel I sent you.” Or it might be raw Bayer, which means I’m just sending you the raw pixel. So “its a red pixel and it has an 8 or a 10-bit value and when I tell you about all the red green and blue, you use your own interpolation to get your color out of that raw data. The point is the digital standard has to know what type of pixel not just the pixel and the value. Digital cameras can also provide advanced features. Again because they are being driven essentially by a small microprocessor inside - whether it is physically a microprocessor or its another type of logic device like an FPGA or an ASIC - there is a device inside driving all the digital electronics, clocking the imager, and correspondingly we can talk to that device and we can build features in. Most notably some of the basic features that are most commonly used are things like test patterns. So in a digital system I can go turn the test pattern on in the camera and I should see on the other end, and if I don’t I can start troubleshooting about were my signal went. But it also includes things like timestamps, frame counters, settings in my I/O ports if I have some input output connected to the camera, things like error checking – I can check if I sent it across but at the end of receiving a frame I can have a checksum or some kind of error checking and check back to make sure that I didn’t introduce errors in the middle that frame or miss a pixel. All types of things come with digital benefits. Digital cameras can do onboard processing as well. Again because there’s a brain they are often able to get all the I/O and the stuff I need to make a good camera. I had to use a bigger processor inside the camera in order to get all the structure to interface it to the outside world but – I have processing left over inside and then what you see is manufacturers trying to make unique things to separate themselves from other manufacturers and attack various applications with the features we can then build into these smart digital devices. In this case you’re seeing here this is actually two CCD camera or two CCDs align per pixel on a prism and getting two simultaneous images. The one images being sent at a high gain and slower exposure so I can see inside the car and see the faces, while the other image is out here where I can see the headlights and presumably license plate if I wanted - but it’s too dark to see inside the car and with the range of a single sensor I can see both. If I turn it up to see the faces then I washout around the headlights and if I were trying to read the license plate when see a yet if I turn it down to see those features I can’t see the faces at all. This camera then because as a digital brain and processors is able to take the two images – fuse them into one high dynamic range image - where I can see everything inside the lights, the glance on the plate but also inside the car and because this is done in the camera, it is served up to the user as a high dynamic range and all of the sudden the user as a camera that attracts an application that was pervious previously a problem for a single camera. In the analog realm we simply couldn’t do this kind of processing. This is again an example of what’s going on in that camera - looking at a lightbulb again - if we want to see the filament we have to use a very fast shutter, very low gain to see the filament without it glowing, but if I need to read the text there is no chance that the camera can align the two on the prism, do it, and read it all out on one images the user Regency everything needs including the glass around the light bult – you can see the fine detail printed, everything. This again is made possible because we have a smart processor in the middle and I can do image processing and natural fusion -feed it for a memory circuit and now the rest of this would be typical output for the camera. The physical interface and a format in this case Gigabit Ethernet vision.

现在我们要讲图像质量的基础知识。其中包括时间噪声。时间噪声是除了光以外的任何可以随时间改变像素值的东西。诸如温度、随机噪声,热噪声,我们开始看到相机的传感器获得热了在阳光下会看到噪音水平上来这出现像电视屏幕上的雪——这是颞噪声像素值变化不稳定,其改变随着时间的推移,温度。空间噪声通常被称为固定模式噪声。其固定。它仍然是一个噪声源。这个像素的值不是某个值，而是它的常数。这个值不会改变，所以这里仍然有一个错误，我们可以纠正诸如模式噪声之类的错误。这就是CMOS传感器通常有固定模式噪声校正的地方。 They also have some more advanced corrections sometimes and what they are going for is the spatial noise, where they know that there is noise that is not real. They have to get that out of the image to get a higher quality image, yet they know their sensor and they know the parameters around them and they can apply a correction because it is fixed. Temporal noise cannot be mathematically corrected out. It might be averaged out over a number frames or some other corrections but in a single frame you cannot mathematically correct temporal noise because of the random nature. Some sources of temporal noise - shot noise or photon noise - a lot of people don’t recognize you hear this and see this but they don’t recognize that this is due to the nature of light. Shot noise is nothing to do with the camera. What it amounts to is light is a wave as we described the very beginning, and waves can cancel out. So if I have a bunch of rays coming at my camera, at a certain light level than a certain number of those rays before they get the camera get out of phase of each other and actually cancel each other out. Then this doesn’t register on the camera. So in bright light if I have a large number of pixels filled up with 40,000 electrons and I have 40,000 rays of light coming at me, you don’t see this random cancellation - you’re not very affected by shot noise. But as people turn the gains up - I get a lowlight situation and maybe I have a 40,000 electrons well, but I’m going to use five or six thousand electrons in that well and I turned the gain up at the end to give my image brightness back - then all of the sudden I have low photon counts. You can see that some of them canceled and again you see this is a random noise source because in this pixel a few canceled, in that one they didn’t cancel that many that are not brighter So you see random variation from pixel to pixel that is just based on the nature of light. Again this is called shot or photon noise. Dark current noise then varies by sensor - in the example here it says - every HC that are current noise doubles, and that’s an average value that varies by sensor. lows usually in that range and what’s happening is the sensors collecting those photons, converting them to a voltage or current for the device, but no matter whether it’s a CCD or CMOS imager, it needs to collect those lights and readout a value for them but during the time that is holding the charge and getting ready to read the value out, there’s always some – what we call - leakage current leaking in here. That’s called dark current noise and that’s because there shouldn’t be any even if I cap the camera and output a black image we can measure the electrons that are flowing into the well. This is essentially the fact that my bucket is not really a bucket - its little bit leaky. You can imagine maybe a well built with bricks or something where there is a little bit of light coming in the side, so if I hold the light for a long – say I do a four second integration to give a real lowlight image in a scientific type application - you can actually measure those photons creeping into the wall or those electrons that never really were photons, but they creep in the well anyway and get measured and that’s called dark current noise. That creates a fixed offset to the camera but again because this varies from every pixel its still a temporal noise source because it’s not perfectly uniform from pixel to pixel. Then there is also quantization noise and that’s errors coming from the A to D conversion process. So whether it is a CCD or is CMOS imager there’s still on analog-to-digital converters somewhere in the CMOS imager of the analog digital conversions taking place on the chip. Whereas in a CCD camera the charge is being read out of the chip and is taking place inside the camera. Both have an A to D converter, and an A to D converter always has a little bit of what we call quantization noise, and that’s where its being affected by some of these thermal effects as well, causing a little bit of noise.

提示：使用更好的A到D转换器来获得较少的量化噪声。

这是根本重要的，因为当我刚刚告诉你，在CMOS相机中，A到D转换器内置该芯片。芯片上的所有这些像素都不只有大量平行的那些，这根本基本上是它们更快的原因。然而，通过本质，它们必须保持结构非常简单且非常小，以适应像素电平或列电平，因此CMOS成像器上的片上A到D转换器不靠近靠近靠近的关闭芯片，离散A到D转换器，我们可以构建到CCD相机中。此外，在CCD相机中，只有一个流通过一个A到D转换器，只有一个流，所以没有从A到D转换器的变化。因此，量化噪声是CCD和CMOS相机之间的差异。这也是为什么CMOS相机往往比CCD更嘈杂。这在这里夸大了这显然很高。但这是揭示了CMOS成像仪的空间噪声，以及您将显示的内容是对此的小压缩。但是，你的字面上看到是所有这些垂直条纹或来自A到D的柱噪声以及我刚刚与转换器讨论的变体。 All you can see that in there and that’s the primary noise source, there is some effect. I don’t know that bad sensor design is the right word there, but as a sensor design effect to get more or less noise and depending the level of sensor you buy – certainly CMOS cameras can be cheaper and in the less expensive imager you would expect more pattern noise and fewer refinements. Whereas in the higher-quality CMOS imager’s that you see nowadays in machine vision you can expect a better image even if there is still pattern noise in the background. There are also trade-offs and this was not always bad design versus what I wanted. For example were talking about global shutter. If I install a global shutter on a CMOS imager that means I had to add an extra transistor to that imager, because unlike CCD there was no ground plane that transistor now takes up space on the imager pixel surface area which means as a smaller photosensitive area. There is always trade-offs and some of which can results in spatial noise. So I’ve mentioned a couple times a signal-to-noise ratio. The ratio of good signal causes, the signal noise is the ratio of good signal caused by light to the unwanted noise - the most important measurement of image quality for digital cameras. Again we described in section one about the well capacity and having more electrons in the well. The blue electrons in this - this is simulating the well and the readout structure - the blue electrons are good signal I want. This light is coming in, registering in the well. The red ones are bad ones some of these are random and come in from just thermal fluctuations. Some of them are still random but they came from dark current leakage. So some of these might’ve been leakage during the readout. The rest might be some random noise, it might be external noise sources. Whatever it is there are some noise sources that can be divided are the good noise can be divided by that and give you a signal-to-noise ratio. So the higher the signal-to-noise ratio you have the better signal you have from the camera, the better we can digitize and create more gray scale values and creates better cameras for you. This also affects sensitivity. Good camera designs require less light to overcome the noise factor. So if you have a bad cameras design, a high noise, you can’t have good sensitivity because sensitivity means I can measure only one or two photons in their. If I have a whole bunch of photons of noise is harder to measure those few so comes back to camera design. Dynamic range then is the range from the brightest pixels the camera can measure to the darkest pixels the camera can measure. Again I’m coming back to the light bulb example that I gave earlier. But it also serves us in this case as well because what we can see is again the dynamic range of the imagers. The ability to measure between a certain black average black level and how far then - still measuring that black level – measure in the right direction. We can see the camera cannot measure everything to see the glass and the filament but still read text and if we turn up you can read the text but we can’t see this. So we can fuse the images and create a higher dynamic range image, with point beings that any CCD camera still has a dynamic range associated to it. The dynamic range is very closely tied to the signal-to-noise ratio and generally they don’t exceed each other by too far. I am not going to discuss the actual measurement method, but they are related. This again is showing the signal-to-noise ratio and how this affects the camera’s output. This is the point at which the camera has no photons in the well – or the black level of the camera. That output is driven to zero, which doesn’t mean cameras are not necessarily single photon devices – doesn’t mean there might not be like getting there but it means this is the minimum light this camera can detect. This camera is set in more or less a linear mode so as light doubles the output doubles and I have some output, and this is the saturation. This is the point at which the well was full. At this point more photons may continue to hit, but the well can’t hold anymore so I can’t readout anymore. The distance between the minimum and maximum point is then the dynamic range. As we go low in the dynamic range or the signal-to-noise ratio, there’s a point where I’m not getting a signal out of the cameras. A very small signal, the noise floor is fixed in the camera so for given noise - small signal, signal-to-noise ratio is very low down here. As I have more light than I’m getting more signal in that well. The noise level is constant. The signal-to-noise ratio is very high at the end of the curve. If available from the manufacturer then, you can get signal-to-noise ratio measurements. Typically this is only good from the manufactured and depending on the test conditions – it is not usually good to compare one manufacturers curve to another’s and say “oh, all things were equal here.” But if you can get them, either in absolute terms where you could consider one camera to another, or for several cameras from manufacturer you can tell things about the camera then. A camera that is good in low light is not always best in bright light. So you want to consider what is the dynamic range and by using this camera - one point is machine vision always have fixed lighting therefore I can count on this point or it’s still maybe machine vision but maybe it’s on an autonomous robot that is driving around outside or something, so light is changing and I’m concerned about the entire range. You want to consider that when you’re buying cameras. So again you can tell each of these cameras has a different point where it in the light. This is coming down to 1 this is 100 and the things with the highest dynamic range are usually the things with the poorest sensitivity because if I have good sensitivity and I have high gain that means unless I have really big well, my well saturated faster. Typically these things were little bit at odds with each other and you want to pay attention to the cameras - were the data is available anyway and where you are going to use them. This is also the point where we want to mention the EMVA 1288 standard. This is a industrial standard for measuring the image quality of digital cameras. To date this is the only industry-standard that’s available that really is applicable to compare one camera to another. There are some standards that have been out there that measure light sensitivity but they don’t necessarily take noise into account and of course noise can be traded for sensitivity. If I have half the noise in a camera I can than double the gain to get the same noise as another camera and have twice the sensitivity. So noise is a factor in sensitivity. EMVA 1288 is one of the few standards out there that takes everything - the sensitivity, the noise level, everything in the camera and it gives you some metrics based on that. It is testing using a known set of the conditions - light lenses and targets and stuff - so manufacturers cant fool it, like cheating on the lens or some parameters. Again there’s specs out there and the manufacturers always put signal-to-noise ratio on the back of a datasheet but the signal-to-noise ratio you read on a camera datasheet, unless it’s a EMVA 1288, its actually meaningless for comparing one camera to another. Because one set of test conditions wasn’t exactly equal to the other, and even when they kind of give you the basics that will look equal, sometimes it’s still not apples to apples comparing in the background. So EMVA 1288 again standardizes all this in a way that there is no fudge and no fudge factor and you can compare one camera to another. Also to get away from variation, results from multiple cameras must be published to show the level of consistency. So it is the same thing in the security market and glowing cameras we see some the highest standards are out there because they’ve tested a gold camera but the average value of batch might not match the gold camera standard. EMVA testing - they have to publish a batch of cameras and look at the averages of those cameras so that you can know how manufacturing variations are affecting the cameras response in the standard and use that to your benefit. Again this allows customers to compare apples to apples one camera to another, and they can be consistently sure. This is very good - my only concern about the standard is that it takes everything into account - all the noise sources, both special and temporal, camera sensitivities, everything. Some applications care less about things, maybe they are more concerned about the temporal noise and the spatial noise. Even though the spatial noise is being held against the camera in this, so in some cases and some very narrow cases you may want to read in between the lines and not just say, “Well these cameras are equal because they are rated.” But how were they equal, what is this one good at? But again the advantage of EMVA 1288, if you have a full test report on the cameras - you can see so all the parameters and you can make, you have the data to make those judgments.

所以我想简要谈谈的最后一件事是什么是一些基本的相机控件，我想强调基本这个词。我们将谈论收益，曝光和快门和类似的东西。如果我们今天打开一个数码相机，可能有50个不同的用户参数，并且有很多客户可以控制的东西。有很多特殊功能，如我所示的制造业。我们今天不会与那个级别交谈，我们将谈谈 - 这是基本课程 - 所以我们将讨论影响最适合用户第一次设置的图像的基础知识。我们想要谈论的第一件事是获得的，大多数人都了解这一概念。收益是一种类似于音量控制的立体声，所以相机在某些条件下获得一些光线。它的输出，如果图像太暗，您只需在立体声中就像它一样在立体声中变得如此安静，我将恢复音量。增益简单只是一个放大器，因此它放大了视频信号，但也放大了噪声和一切。就像在立体声中一样，如果你响亮的话，歌曲结束你可以听到歌曲之间的静态，相机中也是如此。 If I turn it to high gain I am going to see that fixed noise and this comes back to what we’ve been saying all along about the signal-to-noise ratio and the well output. So if we look at the output signal of the sensor, in percent, and then we look at the output value of the camera either in gray scales or we could call it percent as well. But what is basically happening is, in a low light situation, I’m only filling that well up 25% of what it would be in a bright light situation. So in sunlight I get enough photons that well, that 40,000 electron well is completely full and then I can run a minimum amount of gain, zero of gain and get a full output out of that and get the image. Great! Great imaging. Truth is very seldom are we in those cases. So often in machine vision we want high-speed electronic shutters to freeze the motion, but that also means we blocked out light. So if I take one 10,000 shutter freeze the motion like I showed in that motorcycle picture earlier, than that also means I only collected life for 1/10000 of a second and in 1/10000 of a second, probably 40,000 electrons didn’t register in my pixel well. So that means, “hey I used a high shutter, now I only got 10,000 electrons in my 40,000 wells so I am only at 25% of that well.” So how am I going to get my full output back? I am going to apply 12 DB of gain and multiply that up and get a full-scale count out. But again that means I multiplied the noise up as well. I want to pay attention to the gain. One of the principals in the imaging is that if I can keep the gain low but apply more light in any kind of way. If I can live with a longer shutter speed or better yet if I can just use twice as much light then I may get a better quality image and always its better to increase the light, open the F stop in the lens, or apply twice as many lights then it is to apply gain. However there are downfalls to some of those. Its not always possible, it is not always practical to apply more light. You need a shutter to freeze and it is not practical to limit the shutter. To turn the gain up in the camera at some point and recognize it affects your signal-to-noise ratio, notice that also affects my dynamic range. Again the dynamic range is the ratio between the light that’s black and how much light it takes to saturate the imager. So at full well capacity I have a very high dynamic range if I measure these ratios, but if I’ve only used one quarter of the well and use four times the gain than my curve is steep and the light range, the light it takes to saturate is now only ¼, so the dynamic range is cut by a factor of four as well since the noise floor didn’t necessarily move in the camera. It is important to recognize that because a lot of our customers don’t recognize that turning up the gain affects the dynamic range of the camera. They might think in terms of, “well I knew the signal-to-noise ratio fell.” But they didn’t necessarily understand the dynamic range fell. Forward gain then, increasing gain will increase the visibility of both signal and noise. It does not increase image quality. In fact it decreases it because it amplified the noise. It is always a last resort as I described. Turn up the light, anything you can do, if you can do it and gain may be limited at higher bit depths. So if I have a high bit depth camera, they typically, it doesn’t make sense to have 14 bits on a high gain camera. They typically don’t put as high gains in there. So if we look at this, what’s the practical effect of this? Again this is an amplified image for sample here. But this image was taken with the same camera low light. So if I look inside this lens here, in fact I can see the center of lens, I can see all the gray scale variations in the ring out here and in fact the shiny ring. This is taken by using a longer shutter and lower gain. But now if I turn the gain up and make compensations to the shutter, then this is the same image – notice the exposure is the same - I can still see the ring, the gray scale values are approximately the same, but you can see how much more noises in this image and that comes directly from the gain amplifying the noise in the camera and using a smaller percentage of the well capacity that is available in this particular camera. Exposure time is then the length of time that the sensors open for collecting light, also known as the shutter speed or integration time. Exposure time considerations frame rate may be reduced with increase, so if the camera has a certain frame rate there’s always a certain amount time it can expose before it can readout and have time. Obviously if you’re looking at a camera that has 30 frames a second and you want to do a four second exposure you’re probably not doing a four second exposure at 30 a second. Motion blur is greater with an increase in exposure time. Again were not freezing the motions so if I take a longer exposure time that means an object moving at a fixed velocity through the frame will move further during the longer exposure time, thus causing more blurring. Signal-to-noise ratio is increased with exposure just as I said though. So the advantage of exposure is, I can keep the gain low and get more light in there which gives me a higher signal-to-noise ratio provided I can live with the other two effects above. Paying attention to exposure itself. You will note the note on here that says good and better are always a matter of opinion. So somewhere in here you want to look. If I overexpose the image, I’ve lost so much information that I probably don’t have enough to really make a judgment. Of course if I underexposed that I lose the in the black area. There’s no recovering that whether I clip it black or clip it white that information can’t be gained back. If I have it somewhere in between, then I have information and if it’s not there I can actually apply digital methods to maybe stretch to get it back, at least it isn’t clipped away like it is black or white. However there’s optimum and that’s why we are saying that this is really just a matter of opinion. Probably this image gives me the best all round image here as far as seeing the lens, seeing the rings, seeing the light. But guess what? If there was really some kind of variation in the lens and you can see there actually is in their ism there are a couple of little stripes inside this lens, if I were actually measuring those I might want purposely overexpose the image if I didn’t care about inspecting stuff in the exposed area. This might be the kind of exposure I wanted to see - the variation in the black ring inside of the lens. In fact I can see it best here even though I said this is probably the worst image here. If I were really concerned with only measuring this variation I might only use this image. So be aware exposure affects the quality of your image and the dynamic range. Certainly this is the shortest dynamic range, but it’s also user dependent on what they want to see, so make sure that you’re getting the right exposure that is giving you the most gray scale values over the area of the image that you want to use. Black level, sometimes called brightness – it is almost incorrectly called brightness - adds an offset to the pixel value. So the black level - manufacturers call this different things – it can be called black level, in some cases its simply called offset. This is just a constant that we are applying to the image. It goes back to how black is black. In some cases like if I set a camera up in this room and turn it around and start looking at things like the black frame around this TV, well there’s light coming off this black frame is black our eyes, again there is a certain number of photons are still bouncing off and hitting it. So the camera will also see that, but in some cases maybe I wanted that black frame to be black, so the offset gives the user the ability to adjust and say, “ignore those few photons that are coming off, this really should be black,” and set the right starting point in the image. It does affect the brightness. I don’t like this because in the analog TV days brightness actually affected the white level at the top end of the signals the same way you offset the bottom. Brightness technically is moving the top level. In the digital camera world it because of losses affecting both when I move the offset. If you’re talking to older people that are using analog terminology they might not like it if you mix black level and brightness so. Black level considerations - proper use to ensure the camera accurately measures light when the scene is darker. Again how black is black for the correct point that I’m measuring. A side effect is that it can make the image brighter or darker but not by much. You’re mostly affecting the black levels, if I had five black counts of offset and I’m looking at this frame that only had five or ten counts then I may more than double the output of this frame. Yet when I look at the middle of this image that was starting to saturate in areas where I had 200 counts of light, adding five counts to 200 doesn’t affect it much. So we will gain overall brightness by that offset, but it is going to affect the blocks much more than it is going to affect the saturated part of the image. Again you can see this in this image again what are we inspecting here. If I turn the black level up again I can change the offset just in this black area and I can see this starts coming out. Of course if I change the offset you can see the center is a little bit brighter here but just as I was saying the bright pixels in the center are affected much less than the black pixels in the ring. Again if I were inspecting this and I were concerned about that ring setting the black level maybe as important if not much more important than setting the gain in the exposure. Image format controls the type of image sent from the camera. Its usually specified by color or monochrome and then by bit depth. As we were saying earlier, if we choose a higher bit depth we have more date to transmit or process. Correspondingly that gives us more detail than a lower bit depth where we have limited details, so there’s balancing act between how much transfer and how much processing I want to use, versus the amount of detail on my image or the bit depth of my image. Be wary of anyone wishing to view a 12-bit image on a computer monitor. All monitors can display bits or less. Despite the fact that Sharp tells me the contrast ratio is probably about a million to one on this, its back to your standards and how they measure that. You’re not going to realize that the same is true in your PC - you can get 12 and 14 bit images displayed on your PC, but Microsoft and most programs are locked in around 8-bits. You probably use a special program if you were going to view anything more than eight bits even on your computer monitor. Even than the dynamic range your eyes and your monitor are affected and you have to pay attention to what you’re actually seeing versus what the camera computer vision can see. Certainly a computer vision system at the computer levels – if you are doing math and analysis on an image - can see one bit out of 12 bits that will do the math and figure it for you. Your eyes certainly can’t see one out of 12 bits. Many people think they wouldn’t need 12 bits but don’t - again I see this more and more - some the cheapest cameras in the market have the highest bit depth even when the sensors don’t warrant it and you get people in the consumer market that are shopping by resolution and want the highest resolution. You go, “oh, I want the most bits off the pixel,” and most of the time there over specifying the system, so you want to listen carefully to what they need to do and look at what they need to do and make a balance. More is not better in these cases, it’s a balancing act. It can be better if you need it, but if you don’t need it it’s damaging to your application. So this again is more or less showing images but also notice it is showing the file size associated with this image. These are all monochrome but you notice this again is the one bit image, so it is all black and white, often called thresholding. To find things, if I want to measure this ring this is maybe not bad because I can measure the edge very sharp despite the glares. On a one bit image but only 11 kb per image that I am transferring, so if you multiply this by the video rate - 30 per second or something - not very much data. If I take a four-bit image now it is going to multiply – I got four bits per image and see more gray scale and start making out stuff on the lens more. I make the ring out in much greater detail, but I have now four times the data. It would be 44 kb per image again at 30 frames or something that would multiply up and if I made the image I have very good definition on all of these but at the largest I am 88 K per image times the frame rate of the camera. So these things start affecting the bandwidth and the transfer. Again you want to get good enough and balance this out with your customer. There are certainly shades in between. Color format considerations –so, color cameras used to cost a lot more, especially in the analog days where you had to process that analog signal and do it in the analog realm. There were a lot more components. Today in digital cameras especially in the machine vision world, where the digital cameras are making raw Bayer output, they are as little as $50 more. There is really a difference in the censor the camera electronics are basically the same even though the manufacturer may have to do a little more testing on the color camera. But it is not always something you want. Color images are nice, but they can reduce the resolution. The most common color imagers are Bayer complementary color imagers, which mean that one pixels is red, green or blue and I borrow from my neighboring to get my color. But that inherently means that I borrow neighboring pixels and that math called interpolation than causes some errors, which limit my image. In this if I go read the small black-and-white print and blow it up at the picture level with my color camera I have some color aliasing around the black-and-white edges. In fact one of the points in the slide is to show you that is a function of color. The amount of aliasing you get and how bad it is actually worse on black-and-white then it is on say yellow or whites. This is back to monochrome is 2-D color and color is 3-D color and the amount error you get to Bayer interpolation is a function difference of two 3-D vectors. So this means again depending on the contrast in color I am more or less affected by the Bayer interpolation. Resolution also affects it. Much like – we are talking about signal noise and bandwidth - more pixels achieve higher detail, but not always better because more pixels number one mean the pixels are smaller from the image formats so if I have four megapixel imager in the half inch format compared to a one megapixel in a half-inch, that means the pixel has to be smaller which affects everything - signal and dynamic range. Also means it’s harder for the lens, the lens has to resolved to smaller pixels which means I need a higher end lens and maybe I didn’t always need all these pixels for my application.

也许我可以用更少的时间来完成，如果我不需要它们，那么你应该做一个平衡的动作。因为更高的分辨率意味着更多的数据传输，更多的数据处理，这也意味着更高的分辨率也意味着更高的价格，因为你从别人那里买了更高端的相机和更大的成像仪。所以这归结为一个例子-一个实际的例子-如果客户需要检查这里的这个盒子呢?他正在做颜色检查，以检查他的打印是否正确，他想要确保所有的标识都是正确的，他想要确保颜色符合标准，但他还必须阅读条形码。你要看看极限分辨率如果我们只看条形码，这是250像素平方的条形码这是25像素平方的条形码所以很明显我有机会在25像素读取条形码。我需要在条形码的这个区域有250个像素。但是现在我们假设这个条形码是这个图像总宽度的六倍。如果我有250个像素来覆盖这张图片，那就意味着我可能需要3000个像素来覆盖摄像机中的这张图片所以这两个是相辅相成的。你必须看你想要检查的最小物体是什么，以及分辨率是什么，然后把这个分辨率带入你的整个视野。

总之，您希望准确识别您的应用程序是否低或高端，这意味着一切 - 信号噪声，分辨率，动态范围。低端应用程序可能只需要最小的帧速率和分辨率，以及采样图像来说服并完成作业。高端应用程序需要更深的数据。在更高的应用程序中，请注意您的EMVA报告和相机上的东西。Select your camera appropriately.变得更熟悉最常见的传感器 - 这些是市场上可用的典型柯达和索尼传感器。ICX285可能是市场上最敏感的，但柯达传感器往往具有较大的像素和更高的动态范围。因此，有关您和您的客户的权衡。与相机供应商同时交谈并获取建议，不要只是购买数据表。数码相机装满了功能，许多客户从未认为他们需要 - 再次谈论制造，看看他所说的工作完成，并没有让错误的设置歪曲了图像质量。

包裹第二节。