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Dark-frame subtraction

About: Dark-frame subtraction is a research topic. Over the lifetime, 1216 publications have been published within this topic receiving 20763 citations.


Papers
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Journal ArticleDOI
TL;DR: A new adaptive center-weighted hybrid mean and median filter is formulated and used within a novel optimal-size windowing framework to reduce the effects of two types of sensor noise, namely blue-channel noise and JPEG blocking artifacts, common in high-ISO digital camera images.
Abstract: This paper presents a new methodology for the reduction of sensor noise from images acquired using digital cameras at high- International Organization for Standardization (ISO) and long- exposure settings. The problem lies in the fact that the algorithm must deal with hardware-related noise that affects certain color channels more than others and is thus nonuniform over all color channels. A new adaptive center-weighted hybrid mean and median filter is formulated and used within a novel optimal-size windowing framework to reduce the effects of two types of sensor noise, namely blue-channel noise and JPEG blocking artifacts, common in high-ISO digital camera images. A third type of digital camera noise that affects long-exposure images and causes a type of sensor noise commonly known as ''stuck-pixel'' noise is dealt with by pre- processing the image with a new stuck-pixel prefilter formulation. Experimental results are presented with an analysis of the perfor- mance of the various filters in comparison with other standard noise reduction filters. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1668279)

43 citations

Journal ArticleDOI
TL;DR: This work investigated the effects of specific signal-dependent-noise sources, such as film-grain and speckle noise, on image compression, using JPEG (Joint Photographic Experts Group) standard image compression.
Abstract: The performance of an image compression scheme is affected by the presence of noise, and the achievable compression may be reduced significantly. We investigated the effects of specific signal-dependent-noise (SDN) sources, such as film-grain and speckle noise, on image compression, using JPEG (Joint Photographic Experts Group) standard image compression. For the improvement of compression ratios noisy images are preprocessed for noise suppression before compression is applied. Two approaches are employed for noise suppression. In one approach an estimator designed specifically for the SDN model is used. In an alternate approach, the noise is first transformed into signal-independent noise (SIN) and then an estimator designed for SIN is employed. The performances of these two schemes are compared. The compression results achieved for noiseless, noisy, and restored images are also presented.

43 citations

Journal ArticleDOI
TL;DR: The proposed approach combines color edge detection, bilateral noise filter, and edge enhancement based on suitable color spaces and shows that the proposed approach can effectively reduce the noise while preserving and enhancing edges.
Abstract: Removing noise while preserving and enhancing edges is one of the most fundamental operations of image/video processing. When taking pictures with digital cameras, it is frequently found that the color images are corrupted by miscellaneous noise and, hence, noise filtering is necessary. The difficulty is that usually the filtering will reduce the sharpness of the image. On the other hand, optical lens imperfections are usually equivalent to spatial low pass filters and tend to result in blurred images. It is customary to apply edge enhancement algorithm on the image in order to improve the sharpness, but this process usually increase the noise level as a by-product. In this paper, we present a new integrated approach to address these issues. The proposed approach combines color edge detection, bilateral noise filter, and edge enhancement based on suitable color spaces. The experimental results show that the proposed approach can effectively reduce the noise while preserving and enhancing edges.

43 citations

Patent
07 Dec 1998
TL;DR: In this paper, a method for estimating spatial noise characteristics associated with an image acquired from an unknown digital image acquisition device is proposed. But the method is limited to the case of images acquired from a single digital image.
Abstract: Estimating spatial noise characteristics associated with an image acquired from an unknown digital image acquisition device is accomplished by a method, or system, which: provides predetermined default spatial noise characteristic information of the unknown digital image acquisition device; gathers user information related to the spatial noise characteristics of the unknown digital image acquisition device; gathers, from the acquired image, image data related to the spatial noise characteristics of the unknown digital image acquisition device; generates replacement data in response to the user information and the image data; and updates the predetermined default spatial noise characteristic information with the replacement data

43 citations

Proceedings ArticleDOI
01 Dec 2009
TL;DR: Unlike current methods that directly compare the whole pattern noise signal with the reference one, this work proposes to only compare the large components of these two signals, so that the detector can better identify the images taken by different cameras.
Abstract: Digital image forensics has attracted a lot of attention recently for its role in identifying the origin of digital image. Although different forensic approaches have been proposed, one of the most popular approaches is to rely on the imaging sensor pattern noise, where each sensor pattern noise uniquely corresponds to an imaging device and serves as the intrinsic fingerprint. The correlation-based detection is heavily dependent upon the accuracy of the extracted pattern noise. In this work, we discuss the way to extract the pattern noise, in particular, explore the way to make better use of the pattern noise. Unlike current methods that directly compare the whole pattern noise signal with the reference one, we propose to only compare the large components of these two signals. Our detector can better identify the images taken by different cameras. In the meantime, it needs less computational complexity.

43 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20238
202221
20213
20202
20192
20187