Topic
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 published on a yearly basis
Papers
More filters
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01 Sep 2013TL;DR: In this article, an efficient filtering technique intended for the reduction of mixed Gaussian and impulsive noise in digital color images is presented, utilizing the concept of trimmed cumulative distances assigned to pixels from the local filtering window, which serve as measures of a local image homogeneity.
Abstract: In this paper an efficient filtering technique intended for the reduction of mixed Gaussian and impulsive noise in digital color images is presented. The proposed method is utilizing the concept of trimmed cumulative distances assigned to pixels from the local filtering window, which serve as measures of a local image homogeneity. The filter output is defined as a weighted average of the pixels in the processing window and the weights are constructed using the measures of pixel corruption. The proposed design is very fast and outperforms the state-of-the-art denoising schemes in terms of image restoration quality measures.
5 citations
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TL;DR: Noise content from images corrupted by Gaussian and Speckle noise is estimated based on deriving a generic transfer function that attempts to map the median value of the local noise standard deviation that is calculated on overlapping sub-images of the noisy image to the overall noise deviation in the image.
Abstract: of noise from an image continues to be a challenging area of research in the field of image processing However, noise estimation from images that inherently contains very fine details or which have textured regions is still a challenging task This paper attempts to estimate noise content from images corrupted by Gaussian and Speckle noise The noise estimation technique proposed here is based on deriving a generic transfer function This transfer function attempts to map the median value of the local noise standard deviation that is calculated on overlapping sub-images of the noisy image to the overall noise deviation in the image The results obtained show that the proposed algorithm performs well for different types of images and over a large range of noise deviation Comparison with other known standard techniques in literature is also presented in the paper, which confirms that proposed method provides better noise estimation The approach has been proven to work on images affected by speckle noise as well Results for estimation of speckle noise are also presented in this paper
5 citations
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TL;DR: The algorithm is robust and largely independent of the form of the image, returning the noise function with subcount error across the full dynamic range of a synthetic test image where noise of a known form has been added.
Abstract: An efficient and accurate algorithm for determining the magnitude of noise as a function of signal in an arbitrary digital image is presented and demonstrated The algorithm is robust and largely independent of the form of the image, returning the noise function with subcount error across the full dynamic range of a synthetic test image where noise of a known form has been added The noise performance of a CCD under different image recording and processing conditions is examined using the algorithm The effect of different noise functions on pattern-matching measurements of electronic structure by quantitative convergent beam electron diffraction is investigated
5 citations
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TL;DR: Wang et al. as mentioned in this paper proposed a new image denoising method based on lift-wavelet analysis and median filter technology, aiming at the characteristic of the actual image, which is low contrast, complex background and the high background noise.
Abstract: Aiming at the characteristic of the actual image,which is low contrast,complex back-ground and the high background noise,a new image denoising method based on lift-wavelet analysis and median filter technology is proposed.Firstly,the noise image is decomposed with the lift-wavelet.Second,the high frequency parts of decomposed image are carried on median filter algorithm to improve the removing result of the noise image.The denoising image is obtained to reconstruct the high frequency parts processed and low frequency parts of decomposed image.Finally,the image signal to noise ratio(SNR) and the root-mean-square error(RMSE) and the image gray surface chart are applied to estimate the denoising effect of the near-infrared images.These removing noise methods,such as the ordinary wavelet filter,the median filter and so on,are applied to remove the image noises.The experimental results indicate that this method both can eliminate the actual image noise and maintain image edge information.It can remove effectively noise of the real images.
5 citations
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TL;DR: Noise was generated because of intrinsic restriction for the image reconstruction algorithm, filtered back projection, when no input noise was applied and the result noise was rapidly increased under 0.5% input noise ratio.
Abstract: The filtered back projection in the image reconstruction algorithms for the clinic computed tomography system has been widely used. Noise of the reconstructed image was examined under the input noise for parallel and fan beam geometries. The reconstruction images of 512×512 size were carried out under 360 and 720 projection by the Visual C++ for parallel beam and fan beam, respectively, and those agreed with the original Shepp-Logan head phantom very much. Noise was generated because of intrinsic restriction (finite number of projections) for the image reconstruction algorithm, filtered back projection, when no input noise was applied. Because the result noise was rapidly increased under 0.5% input noise ratio, technologies for reducing noise in CT system and image processing is important.
5 citations