LocalSR: Image Super-Resolution in Local Region
Bo Ji, Angela Yao

TL;DR
This paper introduces LocalSR, a new super-resolution task focusing on enhancing only specific regions of an image, reducing computational costs while leveraging global and local context for better results.
Contribution
The paper proposes CLSR, a novel method for local super-resolution that efficiently combines local, global, and proximity features to improve targeted region enhancement.
Findings
Outperforms ROI-focused variants in quality and efficiency
Reduces computational complexity compared to full-image super-resolution
Effectively leverages global and local context for targeted super-resolution
Abstract
Standard single-image super-resolution (SR) upsamples and restores entire images. Yet several real-world applications require higher resolutions only in specific regions, such as license plates or faces, making the super-resolution of the entire image, along with the associated memory and computational cost, unnecessary. We propose a novel task, called LocalSR, to restore only local regions of the low-resolution image. For this problem setting, we propose a context-based local super-resolution (CLSR) to super-resolve only specified regions of interest (ROI) while leveraging the entire image as context. Our method uses three parallel processing modules: a base module for super-resolving the ROI, a global context module for gathering helpful features from across the image, and a proximity integration module for concentrating on areas surrounding the ROI, progressively propagating features…
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Taxonomy
TopicsImage and Signal Denoising Methods · Advanced Image Processing Techniques · Advanced Image Fusion Techniques
MethodsBalanced Selection · Focus
