A Hybrid Self-constrained Genetic Algorithm (HSGA) for Digital Image Denoising Based on PSNR Improvement

2019 ◽  
pp. 135-153
Author(s):  
Divya Verma ◽  
Virendra Prasad Vishwakarma ◽  
Sahil Dalal
Keyword(s):  
Genetic Algorithm ◽  
Image Denoising ◽  
Digital Image

Digital Image Compression Using a Genetic Algorithm

1999 ◽  
Vol 5 (6) ◽  
pp. 379-383 ◽  
Author(s):  
Cheng Yimin ◽  
Wang Yixiao ◽  
Sun Qibin ◽  
Sun Longxiang
Keyword(s):  
Genetic Algorithm ◽  
Image Compression ◽  
Digital Image ◽  
Digital Image Compression

scholarly journals A well-posed multiscale regularization scheme for digital image denoising

2011 ◽  
Vol 21 (4) ◽  
pp. 769-777 ◽  
Author(s):  
V. Prasath
Keyword(s):  
Image Denoising ◽  
Digital Image ◽  
Regularization Parameter ◽  
Input Image ◽  
Smooth Transition ◽  
Functions Of Bounded Variation ◽  
Edge Preserving ◽  
Regularization Scheme ◽  
Adaptive Parameter ◽  
Well Posed

A well-posed multiscale regularization scheme for digital image denoisingWe propose an edge adaptive digital image denoising and restoration scheme based on space dependent regularization. Traditional gradient based schemes use an edge map computed from gradients alone to drive the regularization. This may lead to the oversmoothing of the input image, and noise along edges can be amplified. To avoid these drawbacks, we make use of a multiscale descriptor given by a contextual edge detector obtained from local variances. Using a smooth transition from the computed edges, the proposed scheme removes noise in flat regions and preserves edges without oscillations. By incorporating a space dependent adaptive regularization parameter, image smoothing is driven along probable edges and not across them. The well-posedness of the corresponding minimization problem is proved in the space of functions of bounded variation. The corresponding gradient descent scheme is implemented and further numerical results illustrate the advantages of using the adaptive parameter in the regularization scheme. Compared with similar edge preserving regularization schemes, the proposed adaptive weight based scheme provides a better multiscale edge map, which in turn produces better restoration.

Exponential linear unit dilated residual network for digital image denoising

2018 ◽  
Vol 27 (05) ◽  
pp. 1
Author(s):  
Aditi Panda ◽  
Ruchira Naskar ◽  
Snehanshu Pal
Keyword(s):  
Image Denoising ◽  
Digital Image ◽  
Residual Network

A hybrid genetic algorithm for image denoising

2015 ◽  
Author(s):  
Jonatas L. de Paiva ◽  
Claudio F. M. Toledo ◽  
Helio Pedrini
Keyword(s):  
Genetic Algorithm ◽  
Image Denoising ◽  
Hybrid Genetic Algorithm

Image denoising based on genetic algorithm

2013 ◽  
Author(s):  
Claudio F. M. Toledo ◽  
Lucas de Oliveira ◽  
Ricardo Dutra da Silva ◽  
Helio Pedrini
Keyword(s):  
Genetic Algorithm ◽  
Image Denoising

scholarly journals Denoising Algorithm Based on Generalized Fractional Integral Operator with Two Parameters

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Hamid A. Jalab ◽  
Rabha W. Ibrahim
Keyword(s):  
Visual Perception ◽  
Integral Operator ◽  
Image Denoising ◽  
Digital Image ◽  
Fractional Integral ◽  
Fractional Integral Operator ◽  
Smoothing Filter ◽  
Generalized Fractional Integral ◽  
Two Parameters

In this paper, a novel digital image denoising algorithm called generalized fractional integral filter is introduced based on the generalized Srivastava-Owa fractional integral operator. The structures ofn×nfractional masks of this algorithm are constructed. The denoising performance is measured by employing experiments according to visual perception and PSNR values. The results demonstrate that apart from enhancing the quality of filtered image, the proposed algorithm also reserves the textures and edges present in the image. Experiments also prove that the improvements achieved are competent with the Gaussian smoothing filter.

Sign in / Sign up

Export Citation Format

Share Document