Image Quality Enhancement using Real-ESRGAN: A Comprehensive Review of Datasets and Approaches

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Yash Chavan; Saili Sable; Ayush Rai; Atharv Mahajan; Sarthak Bharambe

Image Quality Enhancement using Real-ESRGAN: A Comprehensive Review of Datasets and Approaches

Authors: Yash Chavan, Saili Sable, Ayush Rai, Atharv Mahajan, Sarthak Bharambe

Unique Paper ID: 176026
Volume: 11
Issue: 11
PageNo: 6436-6441

Keywords: Image Enhancement Super-Resolution Real- ESRGAN ESRGAN JPEG Artifacts REAL-ESRGAN PSNR SSIM.

Abstract:
Rapid developments in image restoration techniques have helped to reduce noise, compression artifacts, and low-resolution images challenges. Real-ESRGAN extends ESRGAN on practical applications in real-world practice by incorporating a high-order degradation model, synthetic training pipelines, and advanced architectures of discriminator. This article reviews its methodologies, provides comparisons of its performance using state-of-the-art techniques, and surveys recent related literature. Its applications, comparative strengths, and future directions are highlighted to bridge existing gaps in the field.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 6436-6441

Image Quality Enhancement using Real-ESRGAN: A Comprehensive Review of Datasets and Approaches

Available:https://ijirt.org/article?manuscript=176026

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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