Stroke-based Cyclic Amplifier: Image Super-Resolution at Arbitrary Ultra-Large Scales

• URL: http://arxiv.org/abs/2506.10774v1
• Date: Thu, 12 Jun 2025 14:51:10 GMT
• Title: Stroke-based Cyclic Amplifier: Image Super-Resolution at Arbitrary Ultra-Large Scales
• Authors: Wenhao Guo, Peng Lu, Xujun Peng, Zhaoran Zhao, Sheng Li,
• Abstract summary: Prior Arbitrary-Scale Image Super-Resolution (ASISR) methods often experience a significant performance decline when the upsampling factor exceeds the range covered by the training data.<n>We propose a unified model, Stroke-based Cyclic Amplifier (SbCA), for ultra-large upsampling tasks.
• Score: 10.209274379479586
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prior Arbitrary-Scale Image Super-Resolution (ASISR) methods often experience a significant performance decline when the upsampling factor exceeds the range covered by the training data, introducing substantial blurring. To address this issue, we propose a unified model, Stroke-based Cyclic Amplifier (SbCA), for ultra-large upsampling tasks. The key of SbCA is the stroke vector amplifier, which decomposes the image into a series of strokes represented as vector graphics for magnification. Then, the detail completion module also restores missing details, ensuring high-fidelity image reconstruction. Our cyclic strategy achieves ultra-large upsampling by iteratively refining details with this unified SbCA model, trained only once for all, while keeping sub-scales within the training range. Our approach effectively addresses the distribution drift issue and eliminates artifacts, noise and blurring, producing high-quality, high-resolution super-resolved images. Experimental validations on both synthetic and real-world datasets demonstrate that our approach significantly outperforms existing methods in ultra-large upsampling tasks (e.g. $\times100$), delivering visual quality far superior to state-of-the-art techniques.

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