Version 1
: Received: 17 January 2022 / Approved: 27 January 2022 / Online: 27 January 2022 (11:02:58 CET)
How to cite:
El Mahdaoui, A.; Ouahabi, A.; Moulay, M.S. Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints. Preprints2022, 2022010411. https://doi.org/10.20944/preprints202201.0411.v1
El Mahdaoui, A.; Ouahabi, A.; Moulay, M.S. Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints. Preprints 2022, 2022010411. https://doi.org/10.20944/preprints202201.0411.v1
El Mahdaoui, A.; Ouahabi, A.; Moulay, M.S. Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints. Preprints2022, 2022010411. https://doi.org/10.20944/preprints202201.0411.v1
APA Style
El Mahdaoui, A., Ouahabi, A., & Moulay, M.S. (2022). Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints. Preprints. https://doi.org/10.20944/preprints202201.0411.v1
Chicago/Turabian Style
El Mahdaoui, A., Abdeldjalil Ouahabi and Mohamed Said Moulay. 2022 "Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints" Preprints. https://doi.org/10.20944/preprints202201.0411.v1
Abstract
In remote sensing applications, one of the key points is the acquisition, real-time pre-processing and storage of information. Due to the large amount of information present in the form of images or videos, compression of this data is necessary. Compressed sensing (CS) is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, using a minimal number of non-adaptive linear measurements. After this CS process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose in this work, a CS reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by the augmented Lagrangian which avoids the difficult problem of non-linearity and non-differentiability of the regularization terms. The proposed algorithm, called denoising compressed sensing by regularizations terms (DCSR), will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov's algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising, and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach allows to gain up to 25% in terms of denoising efficiency, and visual quality using two metrics: PSNR and SSIM.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
