Real-Time Super-Resolution for Real-World Images on Mobile Devices
Jie Cai, Zibo Meng, Jiaming Ding, and Chiu Man Ho

TL;DR
This paper introduces a real-time super-resolution method for mobile devices that handles diverse real-world degradations, outperforming existing approaches in quality and efficiency.
Contribution
A novel real-time super-resolution approach designed for mobile devices that effectively manages various real-world image degradations.
Findings
Outperforms state-of-the-art methods in PSNR and SSIM
Achieves real-time performance on mobile devices
Handles diverse real-world degradations effectively
Abstract
Image Super-Resolution (ISR), which aims at recovering High-Resolution (HR) images from the corresponding Low-Resolution (LR) counterparts. Although recent progress in ISR has been remarkable. However, they are way too computationally intensive to be deployed on edge devices, since most of the recent approaches are deep learning-based. Besides, these methods always fail in real-world scenes, since most of them adopt a simple fixed "ideal" bicubic downsampling kernel from high-quality images to construct LR/HR training pairs which may lose track of frequency-related details. In this work, an approach for real-time ISR on mobile devices is presented, which is able to deal with a wide range of degradations in real-world scenarios. Extensive experiments on traditional super-resolution datasets (Set5, Set14, BSD100, Urban100, Manga109, DIV2K) and real-world images with a variety of…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced Vision and Imaging · Image Processing Techniques and Applications
