A Simple Plugin for Transforming Images to Arbitrary Scales
Qinye Zhou, Ziyi Li, Weidi Xie, Xiaoyun Zhang, Ya Zhang, Yanfeng Wang

TL;DR
This paper introduces ARIS, a transformer-based plugin that enables existing super-resolution models to perform arbitrary scale image upscaling, improving flexibility and generalization without retraining for each scale.
Contribution
We propose a novel transformer-based plugin module and a self-supervised training scheme that allow super-resolution models to handle arbitrary scales effectively.
Findings
Outperforms existing any-scale super-resolution models on benchmarks.
Maintains original performance on fixed scales.
Successfully extrapolates to unseen scales.
Abstract
Existing models on super-resolution often specialized for one scale, fundamentally limiting their use in practical scenarios. In this paper, we aim to develop a general plugin that can be inserted into existing super-resolution models, conveniently augmenting their ability towards Arbitrary Resolution Image Scaling, thus termed ARIS. We make the following contributions: (i) we propose a transformer-based plugin module, which uses spatial coordinates as query, iteratively attend the low-resolution image feature through cross-attention, and output visual feature for the queried spatial location, resembling an implicit representation for images; (ii) we introduce a novel self-supervised training scheme, that exploits consistency constraints to effectively augment the model's ability for upsampling images towards unseen scales, i.e. ground-truth high-resolution images are not available;…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced Vision and Imaging · Image Processing Techniques and Applications
