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- Title
MULTIFRAME IMAGE RESTORATION USING GENERATIVE ADVERSARIAL NETWORKS.
- Authors
Velammal, M.; Singh, Thiyam Ibungomacha; Patil, Nilesh Madhukar; Pal, Subharun
- 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.
- Subjects
GENERATIVE adversarial networks; IMAGE reconstruction
- Publication
ICTACT Journal on Image & Video Processing, 2023, Vol 14, Issue 1, p3043
- ISSN
0976-9099
- Publication type
Article
- DOI
10.21917/ijivp.2023.0432