CMOS sensors are generally easier to build than CCD sensors. Manufacturers can take advantage of the same processes and infrastructure used to build other types of chips, making it possible to create CMOS sensors at a larger scale and lower cost. CCD sensors have more complex circuitry, which translates to a more complex manufacturing process. As a result, CCD sensors are more expensive to build, limiting their use in low-cost consumer devices.

Modulation transfer functionimageprocessing

I’m going to be doing some work on this FFT stuff this weekend. Can you post a link to an easy to understand paper or website for MTF? I can add it then.

At the same time, the CCD architecture also offers several benefits over CMOS. CCD sensors have long been known for producing less noise, achieving greater light sensitivity and delivering higher image quality. That said, advances in CMOS technology have greatly improved CMOS sensors, and the distinction in quality between the two types is no longer as clear-cut as it once was.

The process of manufacturing CMOS sensors is similar to the process used to build other types of CMOS chips, such as those used for microprocessors or memory modules. CMOS sensors can be built in the same high-volume wafer plants as the other chips, often leveraging similar methods.

One of the chip's most important features, outside of the photodiodes, is the analog-to-digital converter, which is built directly into the circuit. It reads the electron charges collected by the photodiodes and translates them into binary data that is then used to create the digital image. A CMOS sensor typically incorporates other functionalities as well, such as image processing, exposure control or temporary buffering.

MTF imagequality

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Okay, I’ve studied this quite a lot today. Needed is a scalar measure of sharpness/contrast/acutance. Focusing and other lens adjustments would serve to optimize this quantity.

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MTF is a common mathematical measure of image sharpness. It involves differentiation and a Fast Fourier Transform. I hoped to find it in one of the standard or extended libraries but so far have not.

Thank you! I am working on the step by step list. First I’m sifting through all the references and pointers and opinions to come up with an optimum approach. Expect my input shortly.

Okay, I’m assuming that a firmware update that exposes the fft functionality (or better yet, provides a proper MTF method or some other measure of image sharpness) is some time in the future. I’m interested in fiddling with the firmware. Is the process for doing so documented? I’m not finding it.

CMOS sensors are used to capture images in digital cameras, digital video cameras and digital CCTV cameras. They can also be found in devices, such as scanners, barcode readers and astronomical telescopes. In addition, they're used for robotic vision, optical character recognition (OCR), satellite photography and radar imaging enhancement, especially for meteorology. CMOS sensors are also found in internet of things (IoT) devices. These sensors enable IoT devices to respond to image input from the physical environment.

“Misha” makes several references to a paper by Marzillano that describes an efficient method of calculating sharpness: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.7.9921&rep=rep1&type=pdf …reading through that now.

Image mtfformula

Introduction to Modulation Transfer Function | Edmund Optics --less on the slant-edge approach, more relating to resolving line patterns and other classical stuff

A CMOS sensor is an electronic chip that converts photons to electrons for digital processing. The chip is based on complementary metal oxide semiconductor (CMOS) technology, which is widely used for many of today's integrated circuits. CMOS itself is a type of metal-oxide semiconductor field-effect transistor (MOSFET), a common type of field-effect transistor that can act as an electrical switch or amplifier.

Wondering if this might be less compute-intensive than the FFT approach. I’m poking through OpenCV now in search of nuggets. UPDATE: Per the discussion at http://answers.opencv.org/question/5395/how-to-calculate-blurriness-and-sharpness-of-a-given-image/ OpenCV has a function, calcBlurriness, which would do the job. Unfortunately it’s undocumented (OpenCV: Video Stabilization). Trying to ferret out the source now.

Here’s code for an autofocus routine used in microscopy, of interest mostly for how they calculate contrast: https://github.com/micro-manager/micro-manager/blob/master/scripts/autofocus_test.bsh

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Here is something interesting. If you have fast .jpeg compression then the job may already be done. Per Detection of Blur in Images/Video sequences - Stack Overflow (poster Misha), the DCT coefficients provide a measure of the high-frequency components in the image.

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My own application has frame-rate as a priority, so efficiency trumps exactitude for me. I just want to maximize “sharpness” and don’t care much about rigorous compliance with ISO this-and-that. I’d imagine autofocus would have similar priorities. Reading material:

From what I’ve gleaned, there are many approaches to determining image quality (sharpness/contrast/resolving ability). The slanted-edge approach seems to be the most accepted, and is in fact the basis of ISO 12233. Below are some links to browse to get the general drift of it.

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http://www.dougkerr.net/Pumpkin/articles/MTF_Slant_Edge.pdf --a few pages of less-interesting background lead up to a nice description of the slant-edge approach

Qualitative data is descriptive information that focuses on concepts and characteristics, rather than numbers and statistics.

Ah, so, we actually have code for the 2D FFT onboard for phase correlation. If you modify the C firmware the camera can do what you want. The phase correlation file shows an example of this: https://github.com/openmv/openmv/blob/master/src/omv/img/phasecorrelation.c

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MTFcamera

For example, the manufacturing process commonly incorporates photolithography, a production method that uses light to transfer a circuit's pattern to a thin photosensitive layer covering the silicon substrate. This pattern is then transferred to the substrate through a multiphase etching process.

I can do the math easily. I just don’t know what particular steps you’d like me to do. We can’t output graphs on the OpenMV Cam. So, everything needs to boil down to one value.

So I’m googling on [“image based” “auto focus” OR autofocus] and find an intriguing reference to “histogram entropy” as a metric of image sharpness here: http://www.emo.org.tr/ekler/dbdbf7ea134592e_ek.pdf

o A slant-edge image target (black/white, at a small angle vs. the pixel array axes) serves as a step function for the imaging system. How steeply stepped does your imaging system perceive it to be? The sharper the step, the more high-spatial-frequency content in its Fourier transform; blurrier images have less.

That’s hugely helpful. I’m a newbie with this so a firmware approach in the future would be wonderful. Meanwhile I’ll see what I can accomplish! Thank you.

A CMOS sensor contains an array of minuscule cells (photodiodes) that capture photons at their various wavelengths and intensity when the light strikes the chip's surface. A lens is often used to focus the light before it reaches the sensor, as is the case of digital cameras. The CMOS sensor is typically covered by a mosaic array of red, blue and green filters that the chip uses for color detection.

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The photodiodes convert the captured photons into electrons, much like tiny solar cells. Each photodiode is surrounded by transistors that amplify the electron charges and transmit them across the chip via tiny wires in the circuitry. The transistors also reset the photodiodes so they're ready for the next image.

Now, I started this thread asking about MTF. But MTF gives a graph vs. spatial frequency, not the figure of merit desired (though I suppose one could pick a spatial frequency and use the value of that bin for optimization).

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Um, anyway, the FFT code I wrote can do 1d FFTs up to 1024 points. It can do both real->complex and complex->complex ffts. It can also do reverse ffts too.

o MTF, then, boils down to calculating how much high-spatial-frequency content is in the image. Lens quality and setup (focus, etc) is an obviously dominant contributor, but the whole imaging system contributes. In the olden days of analog video connections, even cable quality could have a profound impact.

Image mtfgraph

More broadly, how does one assess the contrast/sharpness of an arbitrary image? Is there a less-fancy approach that might be more applicable to a tiny processor?

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Nice : “One of the properties of the 2-D DCT is that it is separable meaning that it can be separated into a pair of 1-D DCTs. To obtain the 2-D DCT of a block a 1-D DCT is first performed on the rows of the block then a 1-D DCT is performed on the columns of the resulting block.”

and py_mtf/mtf.py at master · weiliu4/py_mtf · GitHub --some Python example codes I’ve been picking through Thanks! Meanwhile I’ll play around with that firmware link you provided. Many thanks for that!

Image mtfcurve

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Also see computer vision - Assessing the quality of an image with respect to compression? - Stack Overflow --post by the same author.

For my application, I need to do a fast calculation of the modulation transfer function of a test image. I’m starting from scratch and would appreciate suggestions on how best to do this.

CMOS sensors are often compared to charge-coupled device (CCD) sensors, especially when discussing digital cameras. CCD sensors have a similar structure to CMOS sensors, but they take a different approach to how they shift electrons off the sensor. These differences result in each type offering both advantages and disadvantages when compared.

The CMOS architecture also reduces the amount of power needed to capture an image. In contrast to CMOS sensors, CCD sensors must actively use power to gather light because the cells are not surrounded by transistors, causing them to be less power-efficient.

MTFlens

For example, Canon has developed a CMOS sensor that locates the photodiodes above the transistor layer, making light collection more efficient. The result is less image noise and better image quality. Such improvements are putting CMOS sensors on par with CCD and helping them make inroads into the higher-end digital camera market and in other areas.

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MTFcurve

o MTF seems to be most often calculated as the normalized FFT of the derivative of the image, but I suppose there might be other measures as well; maybe even the histogram of pixel values could have utility for this (since a perfect step would have pixel values in only two bins, white and black; any intermediate bins with pixels in them would indicate blur). I would imagine that image-based autofocus approaches do something similar. Those have been around for a long time. My nearly 30-year-old Sony handicam had image-based autofocus.

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CMOS sensors are also faster than their CCD counterparts because CMOS chips can incorporate functionality directly into the chip. In addition to capturing photons, they can carry out other operations important to creating quality images. Because of this multifunction design, the electrical charges can be read more easily and quickly, resulting in faster readout times.

After my reading, it seems DCT rather than FFT will give us the info we needed with more efficiency. See https://users.cs.cf.ac.uk/Dave.Marshall/Multimedia/PDF/10_DCT.pdf …As I’m sure you know (it was new to me as of today!), DCT is basis of .jpeg, so an efficient implementation probably already exists in OpenMV.

The key to making effective use of IoT devices is to make sure to start an IoT strategy on the right foot and to understand how the edge and IoT are intertwined with one another.

In addition to the sensor itself, a single CMOS chip can incorporate other functions, which eliminates the need to offload those functions to another device. This can help improve read-out times and reduce power consumption, compared to other types of image sensors.

I might have time to write the code for this tommorrow. If you can work out a high level step by step guide for what you want me to do then I can do that. Note that “compute the PSF” is not a sufficient guide… I’ve seen a lot of details on that but I don’t know what they mean.

If I hadn’t started this thread, how would a machine vision engineer have answered that question? Is there a tried-and-true approach?

How to Measure Modulation Transfer Function (4) « Harvest Imaging Blog --good, concise description of the slant-edge approach