Dubois et al. Morfu et al. CMOS image sensors are becoming ubiquitous, due to reduced costs and increasing demands of multimedia applications, such as webcams, digital still cameras, mobile phones or tablets. CMOS technology offers the flexibility to integrate signal and image processing inside the image sensor.
With technology scales, image processing can be realized at chip level, at column level by dedicating processing elements to one or more columns, or even at pixel-level by integrating a specific processing unit in each pixel. By exploiting the ability to integrate sensing with analog or digital processing , new types of CMOS imaging systems can be designed for machine vision, surveillance, medical imaging, motion capture, pattern recognition among other applications.
This extends the basic concept of electronic camera on a chip proposed by E. Fossum to the more sophisticated concept of smart camera on a chip , also called smart vision chip, including image capture and processing. VerIDIS This recent project aims at designing a fully digital pixel sensor array, achieving an unprecedented density by moving all the electronic circuitry to different layers than the photodiodes themselves.
VerIDIS includes a standard CMOS image sensor layer, a per-pixel analog-to-digital converter layer and a fully digital layer including a bit memory and an array of processors , that are stacked vertically to form a prototype 3D-stacked image sensor. This recent project investigates the design of smart image sensors based on Single-Photon Avalanche Diodes SPAD , capable of single-photon sensitivity and precise photon timing. CAPTIvA plans to use 3D-IC technology to design 1 highly sensitive detectors able to capture photons, and 2 new processing elements able to fully exploit the massively parallel stream of photons.
Main papers : POS Each PE convolves the pixel values in a small neighborhood. The core invention is the dynamic reconfiguration of the convolution kernel masks between each capture, allowing the implementation of a wide range of low-level image processing algorithms. The concept of Smart Camera is intimately linked to the notion of "intelligent camera", suggesting that a Smart Camera does a little more than simply capturing images.
From a technological point of view, the fundamental differences between a smart camera and a standard camera is that a smart camera includes at least one special intelligent image processing unit such as DSP, FPGA or ASIC that runs specific image processing algorithms in order to extract information and knowledge from images.
Building smart cameras still remains a major challenge, requiring highly specialized skills in hardware solid-state image sensors, optics, computer architecture and in software real-time operating systems, image processing algorithms. This projects focuses on a complete FPGA-based smart camera architecture which produces a real-time high dynamic range HDR live video stream from multiple captures.
It embeds multiple captures, HDR processing, data display and transfer for a full sensor resolution 1. The main aim of this project is to develop technologies able to deliver high dynamic range and high resolution CMOS cameras generating low noise images with good colour fidelity. We propose 1 an improved algorithm for dynamic compensation of Fixed Pattern Noise, obtained from a real-time analysis of the captured scene and 2 several hardware implementations on a FGPA-based Smart Camera.
The IMX has a small pixel size of 0. What kind of challenges and breakthroughs did the team encounter in the course of development? We invited the engineers in charge of pixel design, Image processing algorithm, and devices to tell us all about it. According to Sony research. As of announcement on July 23, Masahiko Nakamizo: Pixels are the doorway from the visible real world to the electrical world—they convert the light coming through the lens into an electrical signal. Electrical signals converted by pixels go through analog and digital circuits and ultimately become images.
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This means that it is fundamentally difficult to remove noise that got in at the doorway—at the pixel stage—or to restore signal information that was lost at this stage. Thus, the foundation for making quality images starts with the signal from the pixels. If the pixels miss something, then an accurate image cannot be made. Nakamizo: Supposing that the sensor has the same surface area, the smaller the pixel, the more pixels can fit on an image sensor.
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The more pixels, the higher the image resolution. With smartphone cameras getting more and more sophisticated in recent years, every company has been striving to make pixels smaller to meet the demand for more advanced cameras that are still small enough to fit in a phone. So, in order to stay ahead of the competition, we needed to develop even smaller pixels. With the IMX, we were able to achieve a pixel size of 0. Nakamizo: It was 0. Downsizing even 0. We anticipated that we would need to go smaller based on current trends, so we were able to start internal development prior to getting any specific requests from our customers.
Then, once the decision was made to bring it to market, we accelerated our development. However, the trend of miniaturization is about to enter a turning point. That is, we will eventually reach the limit for simply making pixels smaller and face tradeoffs due to miniaturization. Nakamizo: Yes. One of the drawbacks to miniaturization is that sensitivity declines. So, we are discussing various approaches for creating new value. These include, for example, incorporating sensing functions and leveraging not just high resolution but also the pixels and circuits to produce the most beautiful images overall.
While collaborating with other teams, we are looking for new ways to improve performance by looking for the optimal ways to improve characteristics even more than before. Nakamizo: Whenever endeavoring to make a pixel smaller, we first make a prototype and evaluate it.
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Usually it is a straightforward process in which we reflect the evaluation results into our image design knowledge and design concept, and that is pretty much it. However, during this development, we took the time to hold many discussions with those in charge of signal processing, analog circuits, and digital circuits, to identify anticipated concerns and preliminarily investigate countermeasures to take. In particular, the signal processing hardware specifications are decided in the early stages of design, and once the circuit is set, it cannot be changed, which means that in case any concerns arise we typically have to wait until the next generation of circuitry comes along.
However, this time, we were able to have a full discussion of that issue during the prototype stage. In so doing, we were able to address fundamental issues in a timely fashion while we developed the product. The performance of pixels is also greatly affected by how they are manufactured. So, we made it point to go to the plant and discuss things with the people there on a regular basis to get their feedback and gather information. Anytime we had a problem with the characteristics, we worked closely and tenaciously with the team at the plant to find out if it was due to the manufacturing recipe not being followed accurately or due to manufacturing equipment troubles or other causes.
What is the difference between CCD and CMOS image sensors in a digital camera?
This kind of process is also an important aspect of the role of a pixel designer. Nakamizo: I expect that they will spread to various areas, such as factory automation FA and other manufacturing processes, security, and in-vehicle cameras. Meanwhile, there will also still be room to expand for smartphones, for example, to meet the trend toward multi-lens cameras. The trend of incorporating the sensing function with the imaging function is spreading to cameras across various applications.
In the field of FA and security, there is a need for a different kind of value—not the usual picture quality aesthetic of images for human consumption. For example, in a factory it is important to increase the frame rate and capture subjects moving at high speed without blurring. In such case, there is no need for the image to appear beautiful to the human eye. When measuring distances, certifying labels, or monitoring suspicious individuals, it is more important to detect the subject than to produce beautiful images.
When the purpose is sensing, we have to think about what kind of sensor characteristics are most suitable for the given purpose. We are working together with other teams, as well, to explore the potential of image sensors for sensing applications and deliver new value.
Nakamizo: Personally, I think it is the difference in our pixel performance. Battery operation demands lower power, so the AD operates from a single 3-volt supply. The AD, introduced in the fall of , is intended to be used for both DSC and camcorder designs. A direct ADC input is required in camcorder applications, to digitize analog video signals from a tape or external VCR. The AD, now being sampled at this writing , adds a serial digital interface for programming the internal registers--and features a higher sampling rate.
Two important characteristics of especial interest in imaging applications are noise and nonlinearity. Converter SINAD is tested with a sine-wave input, and includes the effects of distortion of the analog signal, converter distortion due to integral and differential nonlinearity INL and DNL , quantization noise, and thermal noise.
In some cases, to reduce the contribution of thermal noise, multiple data records are averaged. The distortion numbers are not of interest in imaging applications because CCD signals are not sinusoidal in nature, and the front-end of the ADC samples the CCD signal only during a relatively slow-moving portion of the waveform. Wideband noise can be measured using a "grounded-input histogram" test, in which the inputs to the device are grounded, and a histogram is taken of the output data.
The standard deviation of the histogram will give the rms noise level of the device not including the ADC quantization noise. A low-noise AFE can have a thermal noise level comparable to or less than the rms quantization noise of its on-board ADC. AFE noise is important because of its impact on the system's dynamic range. Dynamic range is determined by comparing the maximum signal that can be processed to the minimum signal level that can be resolved in the system.
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Fixed pattern noise due to variations in the dark current of each pixel can be very objectionable in images and should be included in the noise calculation if it is not reduced through calibration techniques. Noise will also be introduced by the amplifier used to buffer the CCD's output signal, though this can be minimized by amplifier choice and circuit techniques.
The noise contribution from the AFE can be found on the product's data sheet, or measured using the grounded input histogram test.
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The ADC's resolution will determine the quantization noise level, which is calculated by dividing the weight of one LSB by square root of Adding all the noise sources in a given bandwidth referred to the same point in the signal chain by root-sum-of-squares gives:. This equation can be used in approximating the achievable dynamic range, to see if the AFE being considered is a good match for the CCD.
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If the largest noise source is three times the next largest, it will be dominant. Understanding which noise sources are dominant will help in the selection of an appropriate AFE. The AFE's linearity will also affect system performance. The nonlinearities of a real ADC can cause artifacts in the digitized image.
Differential nonlinearity DNL is very important, because the human visual system is good at detecting edges or discontinuities in an image. DNL that is poor enough to cause missing codes can cause image artifacts in the digital processing.