通过分步渐进提升分辨率，实现任意缩放比例的高清图像重建。
Self-Cascaded Diffusion Models for Arbitrary-Scale Image Super-Resolution
- 将大尺度缩放分解为多个小步骤，逐步提升图像清晰度。
- 在多种任意缩放任务中，感知质量和失真指标均优于现有方法。
- 适合需要灵活缩放且追求细节一致性的图像增强场景。
任意尺度图像超分辨率旨在将图像放大至任意期望分辨率，相比传统固定尺度方法更具灵活性。基于回归或生成模型的现有方法虽表现良好，但常因单阶段设计需同时处理广泛缩放因子，导致尺度不一致问题。为此，本文提出CasArbi——一种自级联扩散框架，用于任意尺度图像超分辨率。CasArbi将不同缩放因子分解为一系列连续小步，每一步通过坐标条件扩散模型逐步提升分辨率，并实现无缝过渡。该方法采用自一致性引导机制，在推理时生成尺度一致的细节。大量实验表明，CasArbi在感知与失真指标上均超越现有方法，并在多类任意尺度超分辨率基准测试中展现出优异的尺度一致性。代码已开源：https://github.com/junseo88/CasArbi。
原文摘要 · Abstract (English)
Arbitrary-scale image super-resolution aims to upsample images to any desired resolution, offering greater flexibility than traditional fixed-scale super-resolution. Recent approaches based on regression-based or generative models have shown promising results but often suffer from scale inconsistency due to their single-stage formulation, which must handle a wide range of scaling factors simultaneously. To address this, we propose CasArbi, a self-cascaded diffusion framework for arbitrary-scale image super-resolution. CasArbi decomposes varying scaling factors into smaller sequential steps, progressively enhancing the image resolution at each step with seamless transitions for arbitrary scales. CasArbi leverages a coordinate-conditioned diffusion model for learning continuous image representations and adopts self-consistency guidance to generate scale-consistent details at inference time. Extensive experiments show that CasArbi outperforms existing methods in both perceptual and distortion metrics and demonstrates superior scale consistency across diverse arbitrary-scale super-resolution benchmarks. Our code is available at https://github.com/junseo88/CasArbi.
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