Image Restoration using Multilayer Neural Networks with Minimization of Total Variation Approach

Mohammed Debakla 1 Khalifa Djemal 2 Mohamed Benyettou 3
1 IRBG
2 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
3 MOSIM
Abstract : Noise reduction is a very important task in image processing. In this aim, many approaches and methods have been developed and proposed in the literature. In this paper, we present a new restoration method for noisy images by minimizing the Total Variation (TV) under constraints using a multilayer neural network (MLP). Indeed, the obtained Euler-Lagrange functional is resolved by minimizing an error functional. The MLP parameters (weights) in this case are adjusted to minimize appropriate functional and provides optimal solution. The proposed method can restore degraded images and preserves the discontinuities. The effectiveness of our approach has been tested on synthetic and real images, and compared with known restoration methods
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Journal articles
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https://hal.archives-ouvertes.fr/hal-01778031
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Submitted on : Wednesday, April 25, 2018 - 12:32:55 PM
Last modification on : Monday, October 28, 2019 - 10:50:22 AM

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  • HAL Id : hal-01778031, version 1

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Mohammed Debakla, Khalifa Djemal, Mohamed Benyettou. Image Restoration using Multilayer Neural Networks with Minimization of Total Variation Approach. International Journal of Computer Science Issues, IJCSI Press, 2014, 11 (1-2), pp.106--115. ⟨hal-01778031⟩

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