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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.


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Proceedings ArticleDOI
02 Oct 2012
TL;DR: The novel design can be seen as a generalization of the vector median filter and is based on the weighting of the dissimilarity measures between pixels contained in the local filtering window to suppresses the impulsive noise corrupting color images while enhancing their edges.
Abstract: In this paper a new approach to the problem of impulsive noise removal in color images is presented. The novel design can be seen as a generalization of the vector median filter and is based on the weighting of the dissimilarity measures between pixels contained in the local filtering window. The weights assigned to each color sample are decreasing functions of their ranks in an ordered sequence, while the ordering is based on the distance between a given pixel and its neighbors. In this way, the rank weights diminish the influence of outliers on the proposed filter output. Extensive experiments revealed that the new filtering design efficiently suppresses the impulsive noise corrupting color images while enhancing their edges. This unique feature can be utilized in any application in which noise removal combined with edge enhancement is desired.

11 citations

Proceedings ArticleDOI
Christian Hentschel1, Haiyan He1
13 Jun 2000
TL;DR: Noise adaptive algorithms are much more robust, and a good and accurate enough algorithm for noise measurement is a key component.
Abstract: Many image enhancement algorithms easily fail in the presence of noise. Noise adaptive algorithms are much more robust, and a good and accurate enough algorithm for noise measurement is a key component. The proposed algorithms combine good performance with low complexity.

11 citations

Proceedings ArticleDOI
03 Dec 2015
TL;DR: This paper combines two famous denoising models to remove a combination of two types of noises: Gaussian noise and Poisson noise from biomedical images.
Abstract: Today large amounts of digital images are created by various modern devices such as digital cameras, X-Ray scanners, and so on. Noise reduces image quality and result of the processing. For example, biomedical images are a type of digital images. In these images, there is a combination of two types of noises: Gaussian noise and Poisson noise. In this paper, we propose a method to remove these noises. This method is based on the total variation of an image intensity (brightness) function. We combine two famous denoising models to remove this combination of noises.

10 citations

Patent
03 Apr 2007
TL;DR: In this paper, the authors proposed a noise reduction device using two-dimensional and three-dimensional noise reduction processing, which is capable of reducing calculation errors of movement amounts of respective pixels due to an influence of noise.
Abstract: PROBLEM TO BE SOLVED: To provide a noise reduction device using two-dimensional noise reduction processing as well as three-dimensional noise reduction processing, which is capable of reducing calculation errors of movement amounts of respective pixels due to an influence of noise. SOLUTION: A noise reduction device 30 includes: a two-dimensional noise reduction processing part 32 which subjects an image signal inputted from the outside to two-dimensional noise reduction processing; a movement detection part 33 which obtains movement amounts in a time base direction of respective pixels by calculating a difference value between a signal after two-dimensional noise reduction processing and a noise-reduced signal of one frame before; and a feedback coefficient and synthesis coefficient detection part 34 which adjusts a gain of three-dimensional noise reduction processing, to which the image signal inputted from the outside should be subjected, in accordance with movement amounts in the time base direction of respective pixels. COPYRIGHT: (C)2009,JPO&INPIT

10 citations

Journal ArticleDOI
TL;DR: A new detection and filtering algorithm that consists of a two-stage detection scheme that employs second-order difference between pixels to determine the integrity of the image pixels and a noise filtering process that estimates the original value of each noisy pixel utilizing the information gathered from (1).

10 citations


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