Abstract
In this paper, we focus on giving two fixed-point-like methods, using proximal operators, called forward-backward and Douglas-Rachford, for solving the restoration problem for grayscale images corrupted with Gaussian noise model. We discuss how to evaluate proximal operators and provide an example in reconstructed image. The main idea is to choose the classic variational model TVL1 for recovering a true image u from an observed image f contaminated with Gaussian noise. The objective function is a sum of two convex terms: the \({{\ell }_{1}}\)-norm data fidelity and the total variational regularization. The first term forces the final image to be not too far away from the initial image and the second term performs actually the noise reduction. Experimental results prove the efficiency of the proposed work by performing some test by changing the noise levels applied to different images. We notice that the Peak Signal-to-Noise Ratio (PSNR) is used to evaluate the quality of the restored images.
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Acknowledgment
The authors would like to thank the organizers of the conference AIAP’2021 and the anonymous reviewers for their valuable comments and suggestions which greatly improved the quality of the paper. Authors would like to thank too the General Directorate for Scientific Research and Technological Development of the Algerian Republic in general and the ETA research laboratory of Bordj Bou Arreridj University in particular, for all material and financial support to accomplish this work.
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Diffellah, N., Hamdini, R., Bekkouche, T. (2022). Image Restoration Using Proximal-Splitting Methods. In: Lejdel, B., Clementini, E., Alarabi, L. (eds) Artificial Intelligence and Its Applications. AIAP 2021. Lecture Notes in Networks and Systems, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-030-96311-8_40
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DOI: https://doi.org/10.1007/978-3-030-96311-8_40
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