Stroke-based Cyclic Amplifier: Image Super-Resolution at Arbitrary Ultra-Large Scales
Journal:
arXiv
Published Date:
Jun 12, 2025
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.
