ICTACT Journal on Image and Video Processing

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Multiframe Image Restoration Using Generative Adversarial Networks


Affiliations
1 Department of Computer science and Technology, Karpagam College of Engineering, India
2 Department of Computer Science and Engineering, Manipur Institute of Technology, Manipur University Campus, India
3 Department of Computer Engineering, Dwarkadas Jivanlal Sanghvi College of Engineering, India
4 Department of Computer Science and Engineering, Indian Institute of Technology Jammu, India
     

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This paper introduces a novel approach for multiframe image restoration using Generative Adversarial Networks (GANs). Traditional image restoration techniques often struggle with handling complex degradation patterns and noise in images. In contrast, GANs have demonstrated remarkable capability in generating realistic and high-quality images. The proposed method leverages the power of GANs to restore multiframe degraded images by training the generator to learn the underlying clean image from a set of degraded frames. The discriminator collaborates with the generator to ensure the fidelity of the restored output. Experimental results on various datasets show that the proposed multiframe image restoration approach achieves superior performance compared to state-of-the-art methods in terms of image quality and fidelity.

Keywords

Multiframe, Image Restoration, Generative Adversarial Networks (GANs), Degradation Patterns, Fidelity.
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Abstract Views: 56

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  • Multiframe Image Restoration Using Generative Adversarial Networks

Abstract Views: 56  |  PDF Views: 1

Authors

M. Velammal
Department of Computer science and Technology, Karpagam College of Engineering, India
Thiyam Ibungomacha Singh
Department of Computer Science and Engineering, Manipur Institute of Technology, Manipur University Campus, India
Nilesh Madhukar Patil
Department of Computer Engineering, Dwarkadas Jivanlal Sanghvi College of Engineering, India
Subharun Pal
Department of Computer Science and Engineering, Indian Institute of Technology Jammu, India

Abstract


This paper introduces a novel approach for multiframe image restoration using Generative Adversarial Networks (GANs). Traditional image restoration techniques often struggle with handling complex degradation patterns and noise in images. In contrast, GANs have demonstrated remarkable capability in generating realistic and high-quality images. The proposed method leverages the power of GANs to restore multiframe degraded images by training the generator to learn the underlying clean image from a set of degraded frames. The discriminator collaborates with the generator to ensure the fidelity of the restored output. Experimental results on various datasets show that the proposed multiframe image restoration approach achieves superior performance compared to state-of-the-art methods in terms of image quality and fidelity.

Keywords


Multiframe, Image Restoration, Generative Adversarial Networks (GANs), Degradation Patterns, Fidelity.

References