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The best digital cameras today are SLRs (single-lens reflex cameras), which use a movable mirror to guide the same light rays that fall on the sensor into the viewfinder. These cameras normally have precisely ground glass lenses and large, high-quality image sensors. In the right hands, they can shoot amazing pictures, with brilliant colors and pleasing lighting effects, often showing a crisply focused subject and an aesthetically blurred background. But these cameras are big, heavy, and expensive: A good digital SLR (DSLR) with a decent set of lenses–including a standard 50 mm, a wide angle, and a telephoto, for example–can easily set you back thousands of dollars.

Preface DIGITAL STILL CAMERAS AT A GLANCE Kenji Toyoda What Is a Digital Still Camera? History of Digital Still Cameras Variations of Digital Still Cameras Basic Structure of Digital Still Cameras Applications of Digital Still Cameras OPTICS IN DIGITAL STILL CAMERAS Takeshi Koyama Optical System Fundamentals and Standards for Evaluating Optical Performance Characteristics of DSC Imaging Optics Important Aspects of Imaging Optics Design for DSCs DSC Imaging Lens Zoom Types and Their Applications Conclusion References BASICS OF IMAGE SENSORS Junichi Nakamura Functions of an Image Sensor Photodetector in a Pixel Noise Photoconversion Characteristics Array Performance Optical Format and Pixel Size CCD Image Sensor vs. CMOS Image Sensor References CCD IMAGE SENSORS Tetsuo Yamada Basics of CCDs Structures and Characteristics of CCD Image Sensor DSC Applications Future Prospects References CMOS IMAGE SENSORS Isao Takayanagi Introduction to CMOS Image Sensors CMOS Active Pixel Technology Signal Processing and Noise Behavior CMOS Image Sensors for DSC Applications Future Prospects of CMOS Image Sensors for DSC Applications References EVALUATION OF IMAGE SENSORS Toyokazu Mizoguchi What is Evaluation of Image Sensors? Evaluation Environment Evaluation Methods COLOR THEORY AND ITS APPLICATION TO DIGITAL STILL CAMERAS Po-Chieh Hung Color Theory Camera Spectral Sensitivity Characterization of a Camera White Balance Conversion for Display (Color Management) Summary References IMAGE-PROCESSING ALGORITHMS Kazuhiro Sato Basic Image-Processing Algorithms Camera Control Algorithm Advanced Image Processing: How to Obtain Improved Image Quality References IMAGE-PROCESSING ENGINES Seiichiro Watanabe Key Characteristics of an Image-Processing Engine Imaging Engine Architecture Comparison Analog Front End (AFE) Digital Back End (DBE) Future Design Engines References EVALUATION OF IMAGE QUALITY Hideaki Yoshida What is Image Quality? General Items or Parameters Detailed Items or Factors Standards Relating to Image Quality SOME THOUGHTS ON FUTURE DIGITAL STILL CAMERAS Eric R. Fossum The Future of DSC Image Sensors Some Future Digital Cameras References

A new method for charge-coupled device (CCD) image processing is presented, based on on-chip pixel binning and generation of several output image signals from a single sensor exposure. Output images differ by resolution, signal-to-noise ratio and sensor pixels region. The method results in reduced frame readout time and increased signal-to-noise ratio in trade for spatial resolution. When used in low-light imaging conditions the output signal correction should be performed. The correction procedure based on CCD camera calibration is presented. Experimental method implementation with high-resolution CCD is shown along with corresponding results.

Continuous improvements in sensitivity have opened up applications for image sensors such as camcorders, digital still cameras, mobile phones, and surveillance cameras. Even though leading-edge image sensors have reached the noise floor of a few electrons [1,2], a thrust towards darker levels still continues down to an illumination level equivalent to being under a crescent moon (i.e., 10−2 down to 10−4 lux). This requires single-photon detection with typical digital cameras pixel size, i.e., 1.5 to 5µm. Although huge-size pixel [3] or single-photon avalanche photodiode (SPAD) based image sensors [4] have been presented for such a purpose, in general, both have to pay area and dark current penalties. Thus, an image sensor capable of both single-photon detection and normal imaging providing us with a high dynamic range is a huge technological challenge.

In recent years, depth sensing systems have gained popularity and have begun to appear on the consumer market. Of these systems, PMD-based Time-of-Flight cameras are the smallest available and will soon be integrated into mobile devices such as smart phones and tablets. Like all other available depth sensing systems, PMD-based Time-of-Flight cameras do not produce perfect depth data. Because of the sensor's characteristics, the data is noisy and the resolution is limited. Fast movements cause motion artifacts, which are undefined depth values due to corrupted measurements. Combining the data of a Time-of-Flight and a color camera can compensate these flaws and vastly improve depth image quality.

Photography is becoming more and more an end-user capability, with many people having digital cameras on their mobile phones. In general, digital cameras are phasing out traditional film-based equipment. The advantages of digital photography are many: the compactness of the cameras, high reliability, ease of image processing, and transmission of the images via the internet or multimedia messaging. These processes emphasize a big problem which is the ability to photograph an object without the approval of its owner. This unsolved problem has many aspects: the right to privacy, paparazzi, industrial intelligence, and the need to protect objects which have high security sensitivity. This paper describes a method of sensing and then disrupting digital imaging by using a controlled radio frequency transmission. This method can be used in order to create a system that causes a localized malfunction of a digital camera in a specified area so that it will degrade photographic recording done by the digital camera. This method is composed of two stages: coupling the digital camera to a source of intentional electromagnetic interference and generating electromagnetic waves in a specific frequency to interfere with the correct functioning of at least one electronic component of the digital camera.

A digitally developed image is a viewable image (TIFF/JPG) produced by a camera’s sensor data (raw image) using computer software tools. Such images might use different colour space, demosaicing algorithms or by different post processing parameter settings which are not the one coded in the source camera. In this regard, the most reliable method of source camera identification is linking the given image with the sensor of camera. In this paper, the authors propose a novel approach for camera identification based on sensor’s readout noise. Readout noise is an important intrinsic characteristic of a digital imaging sensor (CCD or CMOS) and it cannot be removed. This paper quantitatively measures readout noise of the sensor from an image using the mean-standard deviation plot, while in order to evaluate the performance of the proposed approach, the authors tested against the images captured at two different exposure levels. Results show datasets containing 1200 images acquired from six different cameras of three different brands. The success of proposed method is corroborated through experiments.

As one of important outcomes of the past decades of researches on sensor arrays for digital cameras, the manufacturers of sensor array technology have responded to the necessity and importance of obtaining an optimal fill factor, which has great impact on collection of incident photons on the sensor, with hardware solution e.g. by introducing microlenses. However it is still impossible to make a fill factor of 100% due to the physical limitations in practical development and manufacturing of digital camera. This has been a bottle neck problem for improving dynamic range and tonal levels for digital cameras e.g. CCD cameras. In this paper we propose a software method to not only widen the recordable dynamic range of a captured image by a CCD camera but also extend its tonal levels. In the method we estimate the fill factor and by a resampling process a virtual fill factor of 100% is achieved where a CCD image is rearranged to a new grid of virtual subpixels. A statistical framework including local learning model and Bayesian inference is used for estimating new sub-pixel intensity values. The highest probability of sub-pixels intensity values in each resampled pixel area is used to estimate the pixel intensity values of the new image. The results show that in comparison to the methods of histogram equalization and image contrast enhancement, which are generally used for improving the displayable dynamic range on only one image, the tonal levels and dynamic range of the image is extended and widen significantly and respectively.

The pixels of the retina-like sensor are arranged in different concentric rings, and its output image is in log-polar coordinate. Thus additional residual errors will not be produced when the output image is rotated. Therefore, the retina-like sensor has obvious advantages and wide prospect to be applied in the fields of image rotation and rapid image rotation-elimination. In this paper, a theory concerning the image rotation of the retina-like sensor is proposed, and a solution based on the theory is presented and realized for image rotation-elimination. Several image rotation-elimination experiments have been done, and it turns out that the solution is simply, accurate and rapid.

This paper reports a laser diffraction measuring system based on a lab made 26M (5120×5120) pixels image sensor with a pixel pitch of 0.95μm. The long distance required in the traditional method to match the surface of the image sensor with large pixels is shortened, and the image sensor with submicron pixels becomes the key to capture the patterns at short range which is still meet the condition of fraunhofer approximation. The system refers to using a digital optoelectronic sensor array to sample the light transmitted through a specimen without the use of any imaging lenses between the object and the sensor planes. The hardware for such an imaging geometry is significantly simpler and much more compact and lightweight than that of conventional measuring system. In addition, an algorithm of image processing, termed super resolution image reconstruction, is used to further improve the resolution and is required to achieve subpixel spatial resolution in measuring system. In the experimental measurement, the final result can reach the accuracy of 0.98%.

For a new kind of retina-like senor camera and a traditional rectangular sensor camera, dual cameras acquisition and display system need to be built. We introduce the principle and the development of retina-like senor. Image coordinates transformation and interpolation based on sub-pixel interpolation need to be realized for our retina-like sensor’s special pixels distribution. The hardware platform is composed of retina-like senor camera, rectangular sensor camera, image grabber and PC. Combined the MIL and OpenCV library, the software program is composed in VC++ on VS 2010. Experience results show that the system can realizes two cameras’ acquisition and display.

Conventional cameras capture images with limited depth of field and no depth information. Camera systems have been proposed that enable additional depth information to be captured with the image. These systems reduce the resolution of the captured image or result in reduced sensitivity of the lens. We demonstrate a camera that is able to capture extended depth of field images together with depth information at each single frame while requiring minimal impact on the physical design of the camera or its performance. In this paper we show results with a camera for mobile devices, but this technology (named dual aperture to recall the major change in the camera model) can be applied with even greater effect in larger form factor cameras.

From 2000 through 2010, the digital imaging era almost completely replaced a magnificent silver halide technology as the de facto means of capturing, storing, and viewing images. The motion picture industry is well on its way to replacing film despite some of its superior imaging characteristics. What probably few saw was the complete dominance of the cell phone camera where the combination of a “smart” phone linked to the global network and many application packages to ease the total navigation of our complex world and a camera that took reasonably good images. This transformation over such a short period of time is a testimony of the power of technology and marketing. In this chapter, the focus will be on the digital camera, be it a simple “point-and-shoot,” a sophisticated single-lens reflex digital camera with 24-million pixels, or large format camera with untold pixels. All the cameras are based on an imaging sensor, CCD or CMOS, technology. Each camera has to find ways to prevent color aliasing introduced by the need of a color filter array, CFA, improving color reproduction, adjusting for different illuminants, reducing noise, and improving the sharpness of the final image. There are no limits to the technologies incorporated into all digital cameras including face detection, automatic red eye removal, and stabilizing mechanisms that remove “jitter” from camera motion. Here, the focus will be six the major aspects of digital imaging: sampling and aliasing, image sharpness and enhancement, noise in digital cameras, noise reduction methods within and outside of the camera, exposure latitude, and, finally, the concept of ISO speed for a camera and a sensor. Keywords: image processing; sharpness; noise; aliasing; ISO speed; exposure latitude; aliasing potential; auto-exposure; autofocus

To realize next-generation highly realistic sensation broadcasting systems, the research and development of 8K ultrahigh-definition television (UHDTV) systems have been promoted. To realize 8K video cameras, 33Mpixel sensors [1-2] and a full-resolution camera system that uses three 33Mpixel sensors [3] have been reported. However, the weight of the camera with three sensors is over 40kg because the camera requires a large-format color-separation prism. To reduce the size of the camera, single-chip imaging is a promising approach, and a compact single-chip 8K camera that weighs only 2kg has already been developed using a color 33Mpixel CMOS image sensor [4]. However, a conventional single-chip camera has a lower image quality than a full-resolution camera because the total pixel count of the single-sensor camera is only one-third of that of the three-sensor camera, and pixel interpolation is required to configure a full-resolution image. In this paper, a 133Mpixel sensor that can be operated at 60fps to realize a full-resolution 8K single-chip camera is described. To achieve both high speed and suitable ADC resolution, 32-column multiplexing analog readout circuitry and 14b high-speed redundant successive approximation register (SAR) ADCs [5] are adopted. As a result, a full-size image with a data rate of 128.71Gb/s at 60fps has been captured.

We present an embedded software-based automatic calibration system for high-end digital still cameras. By using electronic shutter associated with charge-coupled device (CCD), the system can be used to calibrate the characteristics of the sensor, the mechanical shutter, and the lens accurately. It has been built in several camera models and applied on a practical mass production line. The calibration time for each camera is less than 30 seconds and the calibrated cameras can get very high precision parameters for controlling image capture function.

An image sensor architecture with an alternative image scan method, based on Morton (Z) order, is presented. This scan, compared to the conventional row (raster) scan, enables faster and efficient mean (average) computation of square image blocks. Digital averaging is used and the pixel data is read out with either the original resolution, a 2/spl times/2 or a 4/spl times/4 block averaging. A test chip of 128/spl times/128 array has been implemented in 0.35 /spl mu/m CMOS technology, has 15% fill factor, is operated by a 3.3V supply and dissipates 30mW at video rate.

We demonstrate the basic performance of a high S/N ratio CMOS image sensor with a real-time object-categorizing function. The sensor has capabilities of converting all pixels from analog to digital, object separation, and video image capture. The image sensor is expected to be utilized in the communication and storage application fields offering high quality and large volume image data.

This paper relates to correcting colours in digital photography. Three low pass filters are used to prevent noise amplification during colour correction. It is well known that a straightforward application of a low pass filter reduces the sharpness of an image. The filtering is applied in a non-conventional way which naturally compensates for the blur introduced by filtering. This compensation is achieved by combining data from all the colour channels, thus preserving the original sharpness of the image. This method is particularly useful, but not limited to, CMOS image sensor based digital cameras.

This paper presents a time-resolved CMOS image sensor with draining-only modulation (DOM) pixels for tube-less streak cameras. Although the conventional streak camera has high time resolution, the device requires high voltage and bulky system due to the structure with a vacuum tube. The proposed time-resolved imager with a simple optics realize a streak camera without any vacuum tubes. The proposed image sensor has DOM pixels, a delay-based pulse generator, and a readout circuitry. The delay-based pulse generator in combination with an in-pixel logic allows us to create and to provide a short gating clock to the pixel array. A prototype time-resolved CMOS image sensor with the proposed pixel is designed and implemented using 0.11um CMOS image sensor technology. The image array has 30(Vertical) x 128(Memory length) pixels with the pixel pitch of 22.4um. .

Superresolution from plenoptic cameras or camera arrays is usually treated similarly to superresolution from video streams. However, the transformation between the low-resolution views can be determined precisely from camera geometry and parallax. Furthermore, as each low-resolution image originates from a unique physical camera, its sampling properties can also be unique. We exploit this option with a custom design of either the optics or the sensor pixels. This design makes sure that the sampling matrix of the complete system is always well-formed, enabling robust and high-resolution image reconstruction. We show that simply changing the pixel aspect ratio from square to anamorphic is sufficient to achieve that goal, as long as each camera has a unique aspect ratio. We support this claim with theoretical analysis and image reconstruction of real images. We derive the optimal aspect ratios for sets of 2 or 4 cameras. Finally, we verify our solution with a camera system using an anamorphic lens.

We have developed a digital still camera system processing images by a combination of hardware and software to meet the demand of both high speed and flexibility. The system is capable of supporting CCDs with image size from VGA (640/spl times/480) to SXGA (1280/spl times/960), progressive and interlaced type scanning, and color filters of primary RGB and complementary MGCY signals. The still image processing time for YUV conversion is completed in approximately one frame for the progressive scan type CCD.

We face multiple technical tasks when designing and developing digital cameras for consumer use. As would be expected, image quality is a top concern. Digital still cameras fall into two categories. One uses a black box and a camera lens with a CCD (charge-coupled device) back unit that can acquire more than 3 million pixels. Designed for professional and business use, this camera normally costs more than $5,000. The second type of digital camera is designed for consumers and employs a CCD capable of acquiring 350,000 to over 1 million pixels (Mpixels). Because it is easy to handle and carries a reasonable price for the picture quality, its market has grown dramatically in last three years. Here, I discuss the image-capturing system for a consumer-use digital still camera after briefly explaining its history.

Originally designed for NASA, CMOS-technology digital camera systems on a chip hold great commercial application potential.

13 years after the introduction of the first digital airborne mapping camera in the ISPRS conference 2000 in Amsterdam, several digital cameras with more than 100 MPixels per image have been made available. They are now well established in the market and have replaced the analogue mapping cameras. A general improvement in image quality accompanied the digital camera development. The signal-to-noise ratio and the dynamic range are significantly better than with the analogue cameras. In addition, digital cameras can be spectrally and radiometrically calibrated. The use of these cameras required a rethinking in many places. In the recent years, some activities took place that will lead to a better understanding of the cameras and the data produced by these cameras. Several projects, like the projects of the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) or EuroSDR (European Spatial Data Research), were conducted to test and compare the performance of different cameras. This paper will give an overview of standards and alternative approaches in image quality criteria's, and the derivation and use of these parameters.

Previous video cameras were big and heavy. However, CCD image sensors (CCD-ISs) replaced video camera tubes, so that video cameras became smaller and lighter, and video cameras as consumer products were introduced. Moreover, CCD-ISs created a market for digital still cameras and they replaced film cameras because of they were easy to use. However, CMOS image sensors (CISs) were incorporated into cellular phones that did not require high image resolution. CCD-ISs were then used for digital still cameras; however, their market share drastically declined with the appearance of back illuminated CMOS image sensors (BI-CISs) that had a better signal to noise ratio than CCD-ISs. LOGIC chip stacked BI-CISs were launched after that, which could improve the characteristics of cameras, increase their functions, and enable them to be miniaturized. This paper describes the evolution of CISs and their applications utilizing image quality, 3D integration, and wavelength.

RGB-D sensors are becoming more and more vital to robotics. Sensors such as the Microsoft Kinect and time of flight cameras provide 3D colored point-clouds in real time can play a crucial role in Robot Vision. However these sensors suffer from precision deficiencies, and often the density of the point-clouds they provide is insufficient. In this paper, we present a multi-camera system for correction and enhancement of the data acquired from an RGB-D sensor. Our system consists of two sensors, the RGB-D sensor (main sensor) and a regular RGB camera (auxiliary sensor). We perform the correction and the enhancement of the data acquired from the RGB-D sensor by placing the auxiliary sensor in a close proximity to the target object and taking advantage of the established epipolar geometry. We have managed to reduce the relative error of the raw point-cloud from a Microsoft Kinect RGB-D sensor by 74.5 % and increase its density up to 2.5 times.

We propose a two-dimensional location identification method based on the integration of acceleration sensor and video camera data. The loss of the subjects from camera images is often caused by camera's blind spot. On the other hand, acceleration sensor data don't determine the exact position, because integration must be done twice required to convert acceleration data into position data. To resolve the above problem, this paper employs a linear relationship between the acceleration value and the speed of human movements. The linear relationship decreases the amount of integration/differentiation in the data and determines a more exact position.

The National Ignition Facility requires a radiation-hardened, megapixel CMOS imaging sensor-based camera to be a direct physical and operational replacement for the CCD cameras currently used in x-ray streak cameras and gated imaging detectors. Camera electronics were selected to operate up to 10 krad(Si). The camera incorporates a fast dump of the sensor followed by exposure and image readout. This allows the dumping of charge due to the prompt radiation background and then readout of the longer persistence phosphor image from the x-ray diagnostics. Internal timing delays and optical performance were measured for a radiation-tolerant camera, based on the 2k by 2k CMV4000 sensor from CMOSIS Inc.

Image sensors for digital cameras are built with ever decreasing pixel sizes. The size of the pixels seems to be limited by technology only. However, there are also hard theoretical limits for classical miniature camera systems: During a certain exposure time only a certain number of photons will reach the sensor. The resulting shot noise thus limits the signal-to-noise ratio. On the other hand, diffraction sets another limit for image resolution in case that there is enough brightness in the scene. In this work we show that current sensors are already surprisingly close to these limits.

Today many photogrammetric works involved the use of digital cameras due to it's in low cost. Digital camera has been used in many photogrammetric applications such as archaeology, architecture, accident investigation, cultural heritage and etc. Theoretically, most digital camera is known as non-metric camera where the interior geometrical of digital camera is not stable, however, the optical quality is not acceptable. The need for camera calibration has been fundamental requirement for obtaining accurate measurement since the inception of photogrammetry. This study deals with the acquisition and processing of different configuration of digital camera (convergent and generic network) which are position at different height versus different digital camera setup. The accuracy assessment was performed for two high resolution digital cameras which are Nikon D90 and Rollei D30metric5 digital cameras. The height of digital camera is setup at approximately 80cm and 100cm. Finally, this study evaluates the accuracy of the camera calibration based on standard deviation of interior camera calibration parameters. This study shows that high resolution Nikon D90 digital camera which is categorized as low cost could be used in photogrammetric application with good accuracy achievable.

We present a CMOS image sensor that enables a compact 3-dimensional (3D) vision camera system comprising a single set of the sensor and a camera lens. In order to make binocular parallax, which is essential for 3D imaging, the input pupil of the camera lens is presumed to consist of the right-eye and the left-eye domains, where the pixels exclusively receiving light beams from the right-eye domain and those from the left-eye domain, are arranged alternately. In addition, the sensor features an on-chip lenticular lens to split the incident light from the two directions and a Digital Micro Lens [1,2] to focus the split light beams onto the dedicated pixels without significant crosstalk. The fabricated 3D image sensor enables not only successful stereovision imaging in color with sufficiently high sensitivity, but also accurate calculation of distance.

A 1/4-inch format 560 k pixel IT-CCD image sensor was developed with a high-resolution electronic image stabilizing system that uses a driving method and novel process technology. The new pixel design, process, and on-chip color filter technology keeps almost the same characteristics as the conventional 1/3-inch format 360 k pixel IT-CCD image sensor, in spite of the new sensor's reduced pixel-size.

A 1/3-inch progressive scan CCD image sensor has been developed for image capture in computers and multi-functional digital video cameras. This device shows a high vertical resolution of 480 TV lines and 60 frames/sec image capture. The number of effective pixel is suitable for the digital TV standard and the horizontal driving frequency is 13.5 MHz. >

Two newly developed digital ICs have been able to enhance remarkably the operability and reliability of the HDTV camera. Many kinds of fine adjustments, such as the color fidelity, color saturation, and the image quality, are possible by the attachment of a remote control unit. A prototype HDTV camera with a 2/3" image format and a 1.3 M pixel FIT-CCD image sensor, has been made using these ICs. >

Image sensor is device that is widely used for many kinds of applications, as well as mobile phone camera and digital Still camera. The pixel scheme and the required key characteristics can vary depending on the application, and it is important to fabricate the optimal photodiode for the best image quality of the sensor. In this paper, we will explain key factors and techniques of fabricating photodiode, and introduce this factors and techniques as they apply to the actual products.

This paper describes the computation of depth maps for a high-quality reference camera augmented by a set of satellite sensors. The satellite sensors include support cameras, a TOF (time-of-flight) sensor, and a thermal camera, all rigidly attached to the reference camera. There is extensive previous work on computing depth maps with stereo alone, and high-quality results have been achieved. However it has proved difficult to achieve good results for cases such as texture less areas, or similar fore- and background colors. We show that with our proposed sensor fusion we can achieve high quality results. The paper makes two contributions. The first is a method for combining TOF data with multi-camera data that includes reasoning about occlusions, to produce an improved depth estimate near depth discontinuities. The second contribution is to show the benefit of thermal sensing as a segmentation prior. Thermal cameras were formerly high-cost devices but are now available at the same cost as machine vision cameras. This work demonstrates their advantages, particularly for scenes including humans.

Sensor is an important part of the camera module, dead pixels of which affect the image quality to a very great extent, so the production process requires an effective method to detect the dead pixels of sensor. This paper analyzed the characteristics of sensor imaging and researched dead point detection methods, from manual interpretation method and then given an algorithm program interpretation. Integrated to propose a scientific program, which has a positive meaning for improving the detection efficiency and optimize product quality, to detect the dead points of sensor.

The optical-multiplex system is comprised of an image sensor, a multi-lens array and signal processing unit. The key feature of the optical-multiplex system is that each pixel of the image sensor captures multiple data of the object through multi-lenses and the object data is obtained by processing the raw data output from the optical-multiplex image sensor. We are now able to report that our system can improve the signal-to-noise ratio of the image output from the opticalmultiplex system by changing the shading characteristics of the multi-lenses in the optical-multiplex system. In a model of the system for simulation purposes, an optical-multiplex system with five lenses is used. The five lenses are located at the center, upper, lower, left and right above an image sensor. We calculate the signal-to-noise ratio of the image output from the optical-multiplex system by changing the shading characteristics of the four lenses located at the upper, lower, left and right. The best signal-to-noise ratio of this image output by the optical-multiplex system is 8.895 dB better than that of a camera with a single lens. This value is beyond the previous report value of 3.764 dB.

Digital sensors have obviously invaded the photography mass market. However, some photographers with very high expectancy still use silver halide film. Are they only nostalgic reluctant to technology or is there more than meets the eye? The answer is not so easy if we remark that, at the end of the golden age, films were actually scanned before development. Nowadays film users have adopted digital technology and scan their film to take advantage from digital processing afterwards. Therefore, it is legitimate to evaluate silver halide film "with a digital eye", with the assumption that processing can be applied as for a digital camera. The article will describe in details the operations we need to consider the film as a RAW digital sensor. In particular, we have to account for the film characteristic curve, the autocorrelation of the noise (related to film grain) and the sampling of the digital sensor (related to Bayer filter array). We also describe the protocol that was set, from shooting to scanning. We then present and interpret the results of sensor response, signal to noise ratio and dynamic range.

Image sensors for digital cameras are built with ever decreasing pixel sizes. The size of the pixels seems to be limited by technology only. However, there is also a hard theoretical limit for classical video camera systems: During a certain exposure time only a certain number of photons will reach the sensor. The resulting shot noise thus limits the signal-to-noise ratio. In this letter we show that current sensors are already surprisingly close to this limit.

Along with the popularization of digital cameras, the reliable identification of digital image source is becoming increasingly important as an evidence in a court and some help of investigations. In this paper, we propose an enhanced digital camera identification method using the pixel non-uniformity (PNU) noise of image sensors. By clustering the PNU noises, the proposed method extracts the robust features of image sensors to the random noise, scene content, and image processing engine such as the noise reduction. In the experiments, the proposed method shows the high identification accuracy even for latest digital cameras.

Accurately extrinsic camera self-calibration, namely determining the positions and orientations of the networked cameras by themselves, is essential for many applications such as surveillance, intelligent environments and traffic monitoring. This paper describes an efficient, range-free and anchor-free method for self-calibrating the extrinsic parameters of the cameras in a non-overlapping camera sensor networks. The proposed method is based on the method proposed by Ali on the 2004's CVPR. Knowledge of the locations or angles, got from the assisted sensor (accelerometer or angular-accelerometer) installed on the moving object provide additional effective constraints on the optimization problem in order to compute the cameras' poses. Simulation results show that the iteration times, the calibration error, the volume of the data needed by the improved method are far less than the original method. The advantage of the method is that it can be applied even when the target takes sharp turns out of any camera's Field of View (FoV) with little steps.

CMOS image sensors are now used in a variety of camera applications, such as mobile phones, camcorders, and digital still cameras (DSCs). In particular, most digital single-lens reflex (DSLR) cameras and so called “mirror-less” cameras that require a large-format sensor also employ CMOS image sensors due to their lower power consumption and higher readout speed compared to their CCD counterparts.

Digital imaging technology and digital imaging products are advancing at a rapid pace. The progress of digital cameras has been particularly impressive. Image sensors now have smaller pixel size, a greater number of pixels, higher sensitivity, lower noise and a higher frame rate. Picture resolution is a function of the number of pixels of the image sensor. The more pixels there are, the smaller each pixel, but the sensitivity and the charge‐handling capability of each pixel can be maintained or even be increased by raising the quantum efficiency and the saturation capacity of the pixel per unit area. Sony’s many technologies can be successfully applied to CMOS Image Sensor manufacturing toward sub‐2.0 um pitch pixel and beyond.