Paper Title
ENHANCING IMAGE QUALITY THROUGH GAN-BASED SUPER-RESOLUTION WITH INNOVATIVE QUALITY METRICS
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Registration ID: IJNRD_205839
Published ID: IJNRD1706031
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ENHANCING IMAGE QUALITY THROUGH GAN-BASED SUPER-RESOLUTION WITH INNOVATIVE QUALITY METRICS
Abstract
This "Advancing Single Image Super-Resolution (SISR) with GMGAN: Bridging the Gap in Visual Quality" In recent years, Single Image Super-Resolution (SISR) has emerged as a captivating research frontier. Remarkable progress in SISR owes its success to deep learning and the power of Generative Adversarial Networks (GANs). However, despite these advancements, the generated images still exhibit undesirable artifacts.This paper introduces a novel approach named GMGAN to tackle SISR challenges. Our method is designed with the goal of producing images that align more closely with the capabilities of the Human Visual System (HVS). To achieve this, we introduce a quality loss component by seamlessly integrating an Image Quality Assessment (IQA) metric known as Gradient Magnitude Similarity Deviation (GMSD). Notably, this represents the first instance of truly incorporating an IQA metric into the SISR framework.Furthermore, we address the stability concerns inherent in conventional GANs by adopting a variant known as WGAN-GP. Through a series of experiments, we demonstrate that GMGAN, enhanced by quality loss and WGAN-GP, delivers visually compelling results and establishes a new benchmark in the field of Single Image Super-Resolution.
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How To Cite (APA)
Narendra Kumar D N & Anand Bhamashetti (June-2017). ENHANCING IMAGE QUALITY THROUGH GAN-BASED SUPER-RESOLUTION WITH INNOVATIVE QUALITY METRICS. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 2(6), 192-198. https://ijnrd.org/papers/IJNRD1706031.pdf
Issue
Volume 2 Issue 6, June-2017
Pages : 192-198
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Paper Reg. ID: IJNRD_205839
Published Paper Id: IJNRD1706031
Research Area: Engineering
Author Type: Indian Author
Country: -, -, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD1706031.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD1706031
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