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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: This paper presents an approach to de-noise based on averaging of pixels in 5X5 window is proposed and shows how this approach can improve the quality of microscopic images.
Abstract: Image Processing refers to the use of algorithm to perform processing on digital image. Microscopic images like some microorganism images contain different type of noises which reduce the quality of the images. Removing noise is a difficult task. Noise removal is an issue of image processing. Images containing noise degrade the quality of the images. Removing noise is an important processing task. After removing noise from the images, the visual effect will not be proper. This paper presents an approach to de-noise based on averaging of pixels in 5X5 window is proposed.

2 citations

Journal Article
TL;DR: Experiments indicate that proposed filtering method can remove the impulse noise effectively and preserve the image edge, and outperforms some typical improved median fil-tering method.
Abstract: An improved median filtering method is proposed to suppress the impulse noise with low contamination ratios.It determines the threshold adaptively by calculating the average gray and variance of the pixels in the slide window according to the mathematical statistics characteristic,and replaces the pixels which are deemed as impulse noise by the median gray of slide window.Then uses the switch median filtering method to smooth the pixels which are not suit for noise condition in the former step again.Experiments indicate that proposed filtering method can remove the impulse noise effectively and preserve the image edge,furthermore,it brings down the dependency of the image threshold as well as the degree of damage of the image edge,and outperforms some typical improved median fil-tering method.

2 citations

Journal ArticleDOI
TL;DR: In this article, an efficient filter method for salt-and-pepper noise removal is developed by using cubic B-spline, which is employed to check whether the selected pixel is noisy or noise free.
Abstract: In this paper, an efficient filter method for salt-and-pepper noise removal is proposed This method is developed by using cubic B-spline A noise detector is employed to check whether the selected pixel is noisy or noise free In this method, noise free pixels are left unaltered Since not every pixel is filtered, undue distortion can be avoided Noise pixels are subjected to the filtering operation to reconstruct the intensity values of the noisy pixels The noise free pixels are only considered in the filter operation The cubic B- spline is used as a fitting function to generate additional values within the noise free pixels The noisy pixel is replaced by the mean value of these pixel values The window size is selected as 3 X 3 in the first step If all pixels within the window are considered to be noise, then change the selected window size to 5 X 5 If all the pixels within this window are considered to be noise, then the noisy pixel is replaced by the previous resultant pixel Comparison of the given filter with other existing filters is provided in this paper The results demonstrate that the proposed technique can obtain better performances than other existing denoising techniques As a result of this, the proposed method removes the noise effectively even at noise level as high as 97%

2 citations

Patent
13 Jan 2012
TL;DR: In this paper, the authors proposed a method of digital X-ray film noise assessment, which can be used to solve problems concerning processing of digital images obtained with the use of high energy radiation including the X-rays.
Abstract: The present invention relates to digital image processing, can be used to solve problems concerning processing of digital images obtained with the use of high - energy radiation including the X-rays. The present invention relates in particular to noise assessment of digital X-ray films. Method of digital X-ray film noise assessment includes acquisition of an original image; low-frequency filtering of the original image to obtain an estimated image; a noise image development as a difference between the original - and estimated images; by means of morphologic filtering elimination of noise image pixels corresponding to sharp changes in the original image; division of intensity range of the estimated image into intervals, herewith each pixel of the estimated image relates to an appropriate interval; accumulating for each interval some noise image pixels corresponding to estimated image pixels; calculating interval estimations of noise dispersion using accumulated in such an interval noise image pixels; improving interval estimations by the use of removal noise pixels according to σ 3 criteria, robust local linear approximation of interval estimations of noise dispersion, that results in tabular function describing the dependence of noise on signal intensity; calculation on the base of estimated image and obtained tabular function of the dependence of noise on signal intensity the noise map as a pixel-by-pixel noise dispersion estimation of the digital original image. The technical result is the higher rate and quality of the digital x-ray film noise estimation.

2 citations

Book ChapterDOI
13 Jun 2010
TL;DR: The experimental results demonstrate that the new image mixed noise removal algorithm can do better in smoothing mixed noise and preserving details than the classical ones do in subjective aspect and objective aspect, which will lead to its practicable and effective applications in Mixed noise removal and image restoration.
Abstract: The medium mathematics system is another mathematical tool which deals with fuzzy and uncertain problem. According to the analysis of the features of the image mixed noise, this paper introduces a new image mixed noise removal algorithm based on measuring of medium truth scale. It uses the distance ratio function to detect the noise pixel and to restore the image. The experimental results demonstrate that the new image mixed noise removal algorithm can do better in smoothing mixed noise and preserving details than the classical ones do in subjective aspect and objective aspect, which will lead to its practicable and effective applications in mixed noise removal and image restoration.

2 citations


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