A Framework for Image Denoising Using First and Second Order Fractional Overlapping Group Sparsity (HF-OLGS) Regularizer
Denoising images subjected to Gaussian and Poisson noise has attracted attention in many areas of image processing. This paper introduces an image denoising framework using higher order fractional overlapping group sparsity prior to sparser image representation constraint. The proposed prior has a c...
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doaj-07141481bdc048e3b833cb0b0c18d4b82021-03-29T22:30:27ZengIEEEIEEE Access2169-35362019-01-017262002621710.1109/ACCESS.2019.29016918651512A Framework for Image Denoising Using First and Second Order Fractional Overlapping Group Sparsity (HF-OLGS) RegularizerAhlad Kumar0https://orcid.org/0000-0003-2496-6275M. Omair Ahmad1https://orcid.org/0000-0002-2924-6659M. N. S. Swamy2https://orcid.org/0000-0002-3989-5476Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, CanadaDepartment of Electrical and Computer Engineering, Concordia University, Montreal, QC, CanadaDepartment of Electrical and Computer Engineering, Concordia University, Montreal, QC, CanadaDenoising images subjected to Gaussian and Poisson noise has attracted attention in many areas of image processing. This paper introduces an image denoising framework using higher order fractional overlapping group sparsity prior to sparser image representation constraint. The proposed prior has a capability of avoiding staircase effects in both edges and oscillatory patterns (textures). We adopt the alternating direction method of multipliers for optimizing the proposed objective function by converting it into a constrained optimization problem using variable splitting approach. Finally, we conduct experiments on various degraded images and compare our results with those of several state-of-the-art methods. The numerical results show that the proposed fractional order image denoising framework improves the peak signal to noise ratio of an image by preserving the textures and eliminating the staircases effects. This leads to visually pleasant restored images which exhibit a higher value of Structural SIMilarity score when compared to that of other methods.https://ieeexplore.ieee.org/document/8651512/Image denoisingfractional-orderGaussian and Poisson noiseoverlapping group sparsityalternating direction method of multipliers |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ahlad Kumar M. Omair Ahmad M. N. S. Swamy |
spellingShingle |
Ahlad Kumar M. Omair Ahmad M. N. S. Swamy A Framework for Image Denoising Using First and Second Order Fractional Overlapping Group Sparsity (HF-OLGS) Regularizer IEEE Access Image denoising fractional-order Gaussian and Poisson noise overlapping group sparsity alternating direction method of multipliers |
author_facet |
Ahlad Kumar M. Omair Ahmad M. N. S. Swamy |
author_sort |
Ahlad Kumar |
title |
A Framework for Image Denoising Using First and Second Order Fractional Overlapping Group Sparsity (HF-OLGS) Regularizer |
title_short |
A Framework for Image Denoising Using First and Second Order Fractional Overlapping Group Sparsity (HF-OLGS) Regularizer |
title_full |
A Framework for Image Denoising Using First and Second Order Fractional Overlapping Group Sparsity (HF-OLGS) Regularizer |
title_fullStr |
A Framework for Image Denoising Using First and Second Order Fractional Overlapping Group Sparsity (HF-OLGS) Regularizer |
title_full_unstemmed |
A Framework for Image Denoising Using First and Second Order Fractional Overlapping Group Sparsity (HF-OLGS) Regularizer |
title_sort |
framework for image denoising using first and second order fractional overlapping group sparsity (hf-olgs) regularizer |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Denoising images subjected to Gaussian and Poisson noise has attracted attention in many areas of image processing. This paper introduces an image denoising framework using higher order fractional overlapping group sparsity prior to sparser image representation constraint. The proposed prior has a capability of avoiding staircase effects in both edges and oscillatory patterns (textures). We adopt the alternating direction method of multipliers for optimizing the proposed objective function by converting it into a constrained optimization problem using variable splitting approach. Finally, we conduct experiments on various degraded images and compare our results with those of several state-of-the-art methods. The numerical results show that the proposed fractional order image denoising framework improves the peak signal to noise ratio of an image by preserving the textures and eliminating the staircases effects. This leads to visually pleasant restored images which exhibit a higher value of Structural SIMilarity score when compared to that of other methods. |
topic |
Image denoising fractional-order Gaussian and Poisson noise overlapping group sparsity alternating direction method of multipliers |
url |
https://ieeexplore.ieee.org/document/8651512/ |
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