Optimal sub-band adaptive thresholding based edge preserved satellite image denoising using adaptive differential evolution algorithm

An image is often corrupted by different kinds of noise during its acquisition and transmission. Conventional denoising methods can suppress the Gaussian noise effectively, but fail to maintain the quality of denoised images and may blur edges in an image. To address these short comings, this paper...

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Vydáno v:Neurocomputing (Amsterdam) Ročník 174; s. 698 - 721
Hlavní autoři: Bhandari, A.K., Kumar, D., Kumar, A., Singh, G.K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 22.01.2016
Témata:
Adaptive differential evolution algorithm
Adaptive thresholding function
DWT
Image denoising
Optimization algorithms
DWT
Optimization algorithms
Adaptive differential evolution algorithm
Adaptive thresholding function
Image denoising
ISSN:0925-2312, 1872-8286
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Shrnutí:An image is often corrupted by different kinds of noise during its acquisition and transmission. Conventional denoising methods can suppress the Gaussian noise effectively, but fail to maintain the quality of denoised images and may blur edges in an image. To address these short comings, this paper aims to develop an optimized adaptive thresholding function based framework for edge preserved satellite image denoising using different nature inspired algorithms which is capable of effectively removing the Gaussian noise from images without over smoothing edge details. Image denoising using adaptive thresholding functions selects the suitable threshold values to separate noise from the actual image without affecting the actual features of the image. In this approach, most widely used nature inspired optimization algorithms are exploited for learning the parameters of adaptive thresholding function required for optimum performance. It was found that the proposed adaptive differential evolution algorithm (JADE) algorithm based denoising approach has superior features and give better performance in terms of PSNR, MSE, SSIM and FSIM as compared to other methods.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2015.09.079