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

Bhandari, Ashish Kumar; Kumar, Deepak; Kumar, Anil; Singh, Girish Kumar

doi:10.1016/j.neucom.2015.09.079

Cited by 48 publications

(40 citation statements)

References 57 publications

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“…At a reasonable threshold, threshold image coefficients are set to zero, and the threshold value is subtracted from the image coefficients beyond the threshold value. The graphical representation of soft thresholding as shown in Figure indicates that a wavelet coefficient w between − k and k is set to zero, while the others shrink in absolute value.…”

Section: Methodsmentioning

confidence: 99%

Denoising and segmentation of MR images using fourth order non‐linear adaptive PDE and new convergent clustering

Kollem

Reddy²,

Rao

2018

Int J Imaging Syst Tech

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At present, digital image processing plays a vital role in medical imaging areas and specifically in magnetic resonance imaging (MRI) of brain images such as axial and coronal sections. This article mainly focused on the MRI brain images. The existing methods such as total variation (MC), parallel MRI, modified pyramidal dual‐tree direction filter, adaptive dictionary selection algorithm, classifier methods, and fuzzy clustering techniques are poor in image eminence and precision. Thus, this article presents a novel approach consisting of denoising followed by segmentation. The objective of these proposed methods was visual eminence improvement of medical images to examine tumor extent using an adaptive partial differential equation (APDE)‐based analysis with soft threshold function in denoising. The fourth order, nonlinear APDE was used to denoise the image depending on gradient and Laplacian operators associated with the new adaptive Haar‐type wavelet transform. A second approach was the new convergent K‐means clustering for segmentation. The convergent K‐means procedure diminishes the summation of the squared deviations of structures in a cluster from the center. The significance of these proposed methods was to compute their performances in terms of mean squared error, peak signal‐to‐noise ratio, structure similarity, segmentation accuracy, false hit, missed‐term, and elapsed time. The results were analyzed with the MATLAB software.

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Section: Methodsmentioning

confidence: 99%

Denoising and segmentation of MR images using fourth order non‐linear adaptive PDE and new convergent clustering

Kollem

Reddy²,

Rao

2018

Int J Imaging Syst Tech

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“…Rebuilding of new lowlow sub-band is carried out by taking the inverse SVD on illumination scaled U E , and V E as (21) Inverse DWT on , , , reconstructs the contrast enhanced, edge preserved, denoised satellite image. The steps involved in the proposed approach are well documented in the Fig.2.…”

Section: Let (Ll O Lh O Hl O Hh O ) Be the Sub-bands Of Originamentioning

confidence: 99%

DWT- SVD Based Contrast Enhancement of Satellite Images With Optimal Shape Tuning and Thresholding Parameters Using Ant Lion Optimization Algorithm

Jayanthi¹

2018

ijarcs

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“…The first step of DE algorithm is called initialization, lower (L) and upper (U) bound of each solution are defined and then randomly generate initial populations P and should cover all entire solutions (i.e. , , , , ) (Bhandari et al, 2016).…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

Biomedical images enhancement based on swarm optimization and differential evolution technique

Alotaibi¹

2018

Int. j. adv. appl. sci.

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In this paper, we have introduced an effective technique to remove the noise in the MRI and CT medical images during the process of acquisition, transmission, storage or compression. Removing these noise from medical images must be done without affecting relevant features of the image. Many techniques, such as genetic algorithm, Particle Swarm Optimization, Dynamic Multi-Swarm Particle Swarm Optimization and matching pursuit algorithm are used for denoising MRI and CT images. These techniques need more time to remove noise from medical images and find local point optimal. The proposed Differential Evolution based on Matching Pursuit (DE-MP) is used to detect best atom dictionary. The initial dictionary is created from an anisotropic atom. To evaluate the performance of the proposed techniques, the results of the proposed algorithm were compared with the other algorithm. The numerical results show that the performance of the proposed algorithm is more efficient and faster than other algorithms for medical images denoising.

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

Cited by 48 publications

References 57 publications

Denoising and segmentation of MR images using fourth order non‐linear adaptive PDE and new convergent clustering

Denoising and segmentation of MR images using fourth order non‐linear adaptive PDE and new convergent clustering

DWT- SVD Based Contrast Enhancement of Satellite Images With Optimal Shape Tuning and Thresholding Parameters Using Ant Lion Optimization Algorithm

Biomedical images enhancement based on swarm optimization and differential evolution technique

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