Date of Award
3-18-2025
Document Type
Thesis - SCU Access Only
Publisher
Santa Clara : Santa Clara University, 2025
Degree Name
Master of Science (MS)
Department
Computer Science and Engineering
First Advisor
Nam Ling
Abstract
Super-resolution (SR) is a critical task in computer vision, aiming to reconstruct high-resolution images from low-resolution inputs. While deep learning-based SR models have achieved impressive results, many are computationally expensive and unsuitable for realtime applications. IMDN (Information Multi-Distillation Network) is a lightweight SR model designed for efficiency, but it has limitations in spatial attention, hierarchical feature learning, and edge preservation.
In this work, we propose IMDN-Plus, an enhanced lightweight SR model that integrates Hybrid Attention (CBAM + Swin Transformer) to refine feature extraction, Deeper Feature Distillation to improve texture recovery, and Charbonnier Loss to enhance edge sharpness. These modifications allow IMDN-Plus to achieve better perceptual quality without significantly increasing computational cost.
We evaluate IMDN-Plus on the DIV2K validation set, where it achieves an average PSNR improvement of +0.9 dB and an SSIM gain of +0.0158 over IMDN. Qualitative results further demonstrate sharper textures and reduced artifacts in high-frequency regions. These findings suggest that IMDN-Plus is a practical solution for real-time SR applications, balancing efficiency and image quality.
Recommended Citation
Yu, Peiqi, "IMDN-Plus: Enhancing Lightweight Super-Resolution with Hybrid Attention" (2025). Computer Science and Engineering Master's Theses. 45.
https://scholarcommons.scu.edu/cseng_mstr/45