Fixed-pattern noise

About: Fixed-pattern noise is a research topic. Over the lifetime, 1177 publications have been published within this topic receiving 16551 citations.

Digital camera identification from sensor pattern noise

Abstract: In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's pattern noise. For each camera under investigation, we first determine its reference pattern noise, which serves as a unique identification fingerprint. This is achieved by averaging the noise obtained from multiple images using a denoising filter. To identify the camera from a given image, we consider the reference pattern noise as a spread-spectrum watermark, whose presence in the image is established by using a correlation detector. Experiments on approximately 320 images taken with nine consumer digital cameras are used to estimate false alarm rates and false rejection rates. Additionally, we study how the error rates change with common image processing, such as JPEG compression or gamma correction.

CMOS active pixel image sensors for highly integrated imaging systems

Abstract: A family of CMOS-based active pixel image sensors (APSs) that are inherently compatible with the integration of on-chip signal processing circuitry is reported. The image sensors were fabricated using commercially available 2-/spl mu/m CMOS processes and both p-well and n-well implementations were explored. The arrays feature random access, 5-V operation and transistor-transistor logic (TTL) compatible control signals. Methods of on-chip suppression of fixed pattern noise to less than 0.1% saturation are demonstrated. The baseline design achieved a pixel size of 40 /spl mu/m/spl times/40 /spl mu/m with 26% fill-factor. Array sizes of 28/spl times/28 elements and 128/spl times/128 elements have been fabricated and characterized. Typical output conversion gain is 3.7 /spl mu/V/e/sup -/ for the p-well devices and 6.5 /spl mu/V/e/sup -/ for the n-well devices. Input referred read noise of 28 e/sup -/ rms corresponding to a dynamic range of 76 dB was achieved. Characterization of various photogate pixel designs and a photodiode design is reported. Photoresponse variations for different pixel designs are discussed.

Method and apparatus for compensating for fixed pattern noise in an imaging system

Abstract: The invention relates to a method and apparatus for configuring an imaging system to compensate for fixed pattern noise, variations in pixel values captured from an image sensor that vary according to a fixed pattern. In a method for configuring an imaging system for compensating an additive term component of fixed pattern noise, a pixel array is exposed to a scene of known radiance and an average white value is determined for each pixel of an array. Each average white value is compared to a predetermined reference value to determine a correction value for each pixel. In a method for configuring an imaging system for compensating both an additive and multiplicative component of fixed pattern noise, a pixel array is exposed to a first scene having a first known radiance, and frames of the scene are captured, and then the array is exposed to a second scene having a second known radiance and frames of the second scene are captured. After first and second average white values are determined for each pixel additive and multiplicative term correction values for each pixel are then determined by solving for a system of equations relating the first and second average white values and first and second scene radiances. Correction values can be utilized to correct pixel values of a frame of image data, and a decoding algorithm for attempting to decode a frame of image data including corrected pixel values can be activated.

A 640/spl times/512 CMOS image sensor with ultra wide dynamic range floating-point pixel-level ADC

Abstract: 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/spl times/512 CMOS image sensor with 8-b bit-serial Nyquist rate analog-to-digital 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/spl times/512 sensor is implemented in 0.35-/spl mu/m CMOS technology and achieves 10.5/spl times/10.5 /spl mu/m pixel size at 29% fill factor. Characterization techniques and measured quantum efficiency, sensitivity, ADC transfer curve, and fixed pattern noise are presented. A scene with measured dynamic range exceeding 10000:1 is sampled nine times to obtain an image with dynamic range of 65536:1. Limits on achievable dynamic range using multiple sampling are presented.

Detecting digital image forgeries using sensor pattern noise

Abstract: We present a new approach to detection of forgeries in digital images under the assumption that either the camera that took the image is available or other images taken by that camera are available. Our method is based on detecting the presence of the camera pattern noise, which is a unique stochastic characteristic of imaging sensors, in individual regions in the image. The forged region is determined as the one that lacks the pattern noise. The presence of the noise is established using correlation as in detection of spread spectrum watermarks. We proposed two approaches. In the first one, the user selects an area for integrity verification. The second method attempts to automatically determine the forged area without assuming any a priori knowledge. The methods are tested both on examples of real forgeries and on non-forged images. We also investigate how further image processing applied to the forged image, such as lossy compression or filtering, influences our ability to verify image integrity.