Abstract—Advances in CMOS image sensors enable high-speed image readout, which makes it possible to capture multiple images within a normal exposure time. Earlier work has demonstrated the use of this capability to enhance sensor dynamic range. This paper presents an algorithm for synthesizing a high dynamic range, motion blur free, still image from multiple captures. The algorithm consists of two main procedures, photocurrent estimation and saturation and motion detection. Estimation is used to reduce read noise, and, thus, to enhance dynamic range at the low illumination end. Saturation detection is used to enhance dynamic range at the high illumination end as previously proposed, while motion blur detection ensures that the estimation is not corrupted by motion. Motion blur detection also makes it possible to extend exposure time and to capture more images, which can be used to further enhance dynamic range at the low illumination end. Our algorithm operates completely locally; each pixel’s final value is computed using only its captured values, and recursively, requiring the storage of only a constant number of values per pixel independent of the number of images captured. Simulation and experimental results demonstrate the enhanced signal-to-noise ratio (SNR), dynamic range, and the motion blur prevention achieved using the algorithm. Index Terms—CMOS image sensor, dynamic range extension, motion blur restoration, motion detection, photocurrent estimation, saturation detection. I.

Most of today's video and digital cameras use CCD image sensors, where the electric charge collected by the photodetector array during exposure time is serially shifted out of the sensor chip resulting in slow readout speed and high power consumption. Recently developed CMOS image sensors, by comparison, are read out non-destructively and in a manner similar to a digital memory and can thus be operated at very high frame rates. A CMOS image sensor can also be integrated with other camera functions on the same chip ultimately leading to a single-chip digital camera with very compact size, low power consumption and additional functionality. CMOS image sensors, however, generally su#er from lower dynamic range than CCDs due to their high read noise and non-uniformity. Moreover, as sensor design follows CMOS technology scaling, well capacity will continue to decrease, eventually resulting in unacceptably low SNR.

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ACKNOWLEDGEMENTS I want to thank all the members of my group, my advisor, and my committee for all their help and time.

In this paper, a pixel-parallel image sensor/processor architecture with a fine-grain massively parallel SIMD analogue processor array is overviewed and the latest VLSI implementation, SCAMP-3 vision chip, comprising 128×128 array, fabricated in a 0.35µm CMOS technology, is presented. Examples of real-time image-processing executed on the chip are shown. Sensor-level data reduction, wide dynamic range and adaptive sensing algorithms, enabled by the sensor-processor integration, are discussed. 1.

Analysis results demonstrate that multiple sampling can achieve consistently higher signal-to-noise ratio at equal or higher dynamic range than using other image sensor dynamic range enhancement schemes such as well capacity adjusting. Implementing multiple sampling, however, requires much higher readout speeds than can be achieved using typical CMOS active pixel sensor (APS). This paper demonstrates, using a 640 222 512 CMOS image sensor with 8-b bit-serial Nyquist rate analog-todigital converter (ADC) per 4 pixels, that pixel-level ADC enables a highly flexible and efficient implementation of multiple sampling to enhance dynamic range. Since pixel values are available to the ADC's at all times, the number and timing of the samples as well as the number of bits obtained from each sample can be freely selected and read out at fast SRAM speeds. By sampling at exponentially increasing exposure times, pixel values with binary floating-point resolution can be obtained. The 640 222 512 ...

Several papers have discussed the idea of extending image sensor dynamic range by capturing several images during a normal exposure time. Most of these papers assume that the images are captured according to a uniform or an exponentially increasing exposure time schedule. Even though such schedules can be justified by certain implementation considerations, there has not been any systematic study of how capture time schedules should be optimally determined. In this paper we formulate the multiple capture time scheduling problem when the incident illumination probability density function (pdf) is completely known as a constrained optimization problem. We aim to find the capture times that maximize the average signal SNR. The formulation leads to a general upper bound on achievable average SNR using multiple capture for any given illumination pdf. For a uniform pdf, the average SNR is a concave function in capture times and therefore well-known convex optimization techniques can be applied to find the global optimum. For a general piece-wise uniform pdf, the average SNR is not necessarily concave. The cost function, however, is a Di#erence of Convex (D.C.) function and well-established D.C. or global optimization techniques can be used.

CMOS technology provides the capability to integrate a complete imaging system on a single chip [1,2], and to extend dynamic range [4,5,6]. A CMOS imaging system-on-chip is described that is programmable and capable of producing color video at up to 500 frames/s with over 100dB of dynamic range using multi-capture and sensitivity better than 3 lux. The chip incorporates an image sensor with pixel-level Multi-Capture Bit Serial (MCBS) ADC [6], a micro-control engine, and a full 4.9Mb frame buffer. The chip is fabricated in a 0.18µm CMOS Image Sensor Technology [3]. When paired with a companion image-processing chip, via a digital LVDS interface, the system produces standard NTSC or PAL video output. A photomicrograph of the chip that highlights its major sections is shown in Fig. 12.1.1. The image sensor consists of a 742 x 554 pixel array coupled with a 10b DAC, control line drivers, and

The market for solid-state image sensors has been experiencing explosive growth in recent years due to the increasing demands of mobile imaging, digital still and video cameras, Internet-based video conferencing, surveillance, and biometrics. With over 230 million parts shipped in 2004 and an estimated annual growth rate of over 28 % (In-Stat/MDR), image sensors have become a significant silicon technology driver. Charge-coupled devices (CCDs) have traditionally been the dominant image-sensor technology. Recent advances in the design of image sensors implemented in complementary metaloxide semiconductor (CMOS) technologies have led to their adoption in several high-volume products, such as the optical mouse, PC cameras, mobile phones, and high-end digital cameras, making them a viable alternative to CCDs. Additionally, by exploiting the ability to integrate sensing with analog and digital processing down to the pixel level, new types of CMOS imaging devices are being created for manmachine interface, surveillance and monitoring, machine vision, and biological testing, among other applications. In this article, we provide a basic introduction to CMOS image-sensor technology, design, and performance limits and present recent

Abstract * In this paper we present a CMOS image sensor design with a high dynamic range. This feature is achieved by a two-frame scheme, in which an image is captured twice with different sensitivity levels. One of them focuses on the low light intensity part of the image, while the other focuses on the high light intensity part. After proper composition, these two frames are then combined as a single image with a higher dynamic range. The proposed scheme is a low-cost solution in the sense that it can be built on top of any traditional sensor design with only minor modification made to the on-chip controller. A simulation environment with post-layout accuracy is used to demonstrate the effectiveness. It shows that a number of pictures, e.g., the one of fireworks, can be captured more clearly than a traditional sensor. 1.