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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: Extensive experiments show that the proposed algorithm provides better performance than many of the existing vector filters in terms of noise suppression and preserving thin lines, fine details, and image edges.
Abstract: In this paper, a new method is presented for reducing salt and pepper noise in color images. This method consists of three steps: In the first step, Laplacian operators and threshold values are used to identify pixels that are likely to have been corrupted by noise; in the second step, these noise candidates are judged by using the neighborhood of each pixel. After recognizing the noisy pixels, the vector median filter is used for replacing the noisy pixels, in the third step. The proposed algorithm is tested against different color images, and it gives a better peak signal-to-noise ratio and a lower normalized mean square error. These results have also been checked and analyzed visually. The performance of the proposed method is compared with common existing vector filters at different noise densities. Extensive experiments show that the proposed algorithm provides better performance than many of the existing vector filters in terms of noise suppression and preserving thin lines, fine details, and image edges.

11 citations

Patent
Jae-Han Jung1
12 Nov 2004
TL;DR: In this article, an apparatus and method of measuring noise in video signals includes a highfrequency component determination part that detects a high-frequency component value of a first image to measure noise in a blockwise unit, a spatial filter that filters the first image in the blockwise units, by applying different filtering methods according to the high frequency component value, and outputting the filtered image as a second image.
Abstract: An apparatus and method of measuring noise in video signals includes a high-frequency component determination part that detects a high-frequency component value of a first image to measure noise in a blockwise unit, a spatial filter that filters the first image in the blockwise unit, by applying different filtering methods according to the high frequency component value, and outputting the filtered image as a second image, a motion compensation error determination part that determines a presence of a motion compensation error by comparing a first difference between corresponding pixel values of the first image and the second image with a second difference between corresponding pixel values of the first image and a third image which is motion-compensated image derived from the first image, and a noise calculator that measures noise with reference to the second difference between the corresponding pixel values of the first image and the third image for pixels determined by the motion compensation error determination part to have no motion compensation error.

11 citations

Journal ArticleDOI
01 Aug 1998
TL;DR: In this article, a new domain called the peak-trace domain is introduced to clearly specify the signal dominant region (used for extracting the camera-shaking degree) and the noise dominant region(used for estimating the noise variance).
Abstract: A method is proposed which estimates the degree of motion blur caused by the shaking of a digital camera during the exposure time. A new domain called the peak-trace domain is introduced to clearly specify the signal dominant region (used for extracting the camera-shaking degree) and the noise dominant region (used for estimating the noise variance). The experimental results show that the proposed method offers an efficient way to precisely detect the camera shaking and to restore the noisy blurred image.

11 citations

Proceedings ArticleDOI
18 Dec 2009
TL;DR: The paper describes an stand alone algorithm for Speech Enhancement and presents a architecture for the implementation, which works on streaming speech signals and can be used in factories, bus terminals, Cellular Phones, or in outdoor conferences where a large number of people have gathered.
Abstract: The Paper presents the outlines of the Field Programmable Gate Array (FPGA) implementation of Real Time speech enhancement by Spectral Subtraction of acoustic noise using Dynamic Moving Average Method. It describes an stand alone algorithm for Speech Enhancement and presents a architecture for the implementation. The traditional Spectral Subtraction method can only suppress stationary acoustic noise from speech by subtracting the spectral noise bias calculated during non-speech activity, while adding the unique option of dynamic moving averaging to it, it can now periodically upgrade the estimation and cope up with changes in noise level. Signal to Noise Ratio (SNR) has been tested at different noisy environment and the improvement in SNR certifies the effectiveness of the algorithm. The FPGA implementation presented in this paper, works on streaming speech signals and can be used in factories, bus terminals, Cellular Phones, or in outdoor conferences where a large number of people have gathered. The Table in the Experimental Result section consolidates our claim of optimum resouce usage.

11 citations

01 Jan 2011
TL;DR: A new methodology based on neural network for identifying the different types of noise such as Non Gaussian, Gaussian white, Salt and Pepper and Speckle noise is proposed.
Abstract: Image noise is unwanted information in an image and can occur at any moment of time such as during image capture, transmission, or processing and it may or may not depend on image content. In order to remove the noise from the noisy image, prior knowledge about the nature of noise must be known otherwise noise removal causes the image blurring. Identifying nature of noise is a challenging problem. Many researchers have proposed their ideas on image noise identification and each of the work has its assumptions, advantages and limitations. In this paper, we proposed a new methodology based on neural network for identifying the different types of noise such as Non Gaussian, Gaussian white, Salt and Pepper and Speckle noise.

11 citations


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