Dark-frame subtraction

Abstract: This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction. Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast. Details of the new feature detectors and of the new noise reduction method are described, along with test results.

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.

Abstract: An effective algorithm for digital image noise filtering is presented in this paper. Mostnoise filtering techniques such as Kalman filter and transform domain methods require extensive image modeling and produce filtered images with considerable contrast loss. The algorithm proposed in this paper is an extension of Lee's local statistics method modified to utilize local gradient information. It does not require image modeling, and it will not smear edges and subtle details. For both the additive and multiplicative noise cases, the local mean and variance are computed from a reduced set of pixels depending on the orientation of the edge. Consequently, noise along the edge is removed, and the sharpness of the edge is enhanced. For practical applications when the noise variance is spatially varying and unknown, an adaptive filtering algorithm is developed. Experiments show its good potential for processing real-life images. Examples on images containing 256×256 pixels are given to substantiate the theoretical development.

Abstract: The spectral subtraction method is a well-known noise reduction technique. Most implementations and variations of the basic technique advocate subtraction of the noise spectrum estimate over the entire speech spectrum. However, real world noise is mostly colored and does not affect the speech signal uniformly over the entire spectrum. In this paper, we propose a multi-band spectral subtraction approach which takes into account the fact that colored noise affects the speech spectrum differently at various frequencies. This method outperforms the standard power spectral subtraction method resulting in superior speech quality and largely reduced musical noise.

Abstract: A commonly used tool to conceal the traces of tampering is the addition of locally random noise to the altered image regions. The noise degradation is the main cause of failure of many active or passive image forgery detection methods. Typically, the amount of noise is uniform across the entire authentic image. Adding locally random noise may cause inconsistencies in the image's noise. Therefore, the detection of various noise levels in an image may signify tampering. In this paper, we propose a novel method capable of dividing an investigated image into various partitions with homogenous noise levels. In other words, we introduce a segmentation method detecting changes in noise level. We assume the additive white Gaussian noise. Several examples are shown to demonstrate the proposed method's output. An extensive quantitative measure of the efficiency of the noise estimation part as a function of different noise standard deviations, region sizes and various JPEG compression qualities is proposed as well.