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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: March 2026
Volume 11 | Issue 3
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| Paper Title: | Perceptual Single-Image Super Resolution Using Residual Generative Adversarial Networks |
| Authors Name: | Nitin Varshney , Harsh Mathur |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_209582
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| Published Paper Id: | IJRTI2602006 |
| Published In: | Volume 11 Issue 2, February-2026 |
| DOI: | https://doi.org/10.56975/ijrti.v11i2.209582 |
| Abstract: | Single-image super-resolution (SISR) seeks to reconstruct a high-resolution (HR) image from a single low-resolution (LR) observation, a task that becomes especially challenging at large upscaling factors where textures and fine structures are easily lost. Recent deep CNN methods largely optimize pixel-wise losses such as mean squared error (MSE), which correlates with peak signal-to-noise ratio (PSNR) but often suppresses high-frequency details and yields overly smooth images. Motivated by this limitation, we propose a residual generative adversarial network (ResGAN-SR) that combines deep residual learning with adversarial and perceptual losses for 4× SISR. The generator adopts a ResNet-style architecture with stacked residual blocks and sub-pixel up-sampling, while the discriminator learns to distinguish super-resolved images from real HR counterparts. A composite perceptual loss, formed from VGG-based content features and an adversarial term, guides the generator toward solutions that are both structurally faithful and visually realistic. Experiments on the Div2K dataset demonstrate that ResGAN-SR improves perceptual quality in terms of structural similarity (SSIM), mean opinion score (MOS), and visual sharpness, while remaining competitive in PSNR and MSE compared with a purely residual CNN baseline. These results indicate that explicitly modelling perceptual cues in a GAN-based residual framework is an effective strategy for photorealistic image upscaling in applications such as surveillance, medical imaging, and remote sensing. |
| Keywords: | single-image super-resolution, generative adversarial network, residual learning, perceptual loss, ResNet-GAN |
| Cite Article: | "Perceptual Single-Image Super Resolution Using Residual Generative Adversarial Networks", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.11, Issue 2, page no.a44-a52, February-2026, Available :http://www.ijrti.org/papers/IJRTI2602006.pdf |
| Downloads: | 000127 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2602006
Registration ID:209582
Published In: Volume 11 Issue 2, February-2026
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v11i2.209582
Page No: a44-a52 Country: Rajkot, Gujrat, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2602006 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2602006 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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