Abstract
The effectiveness of super resolution (SR) models hinges on their ability to recover high frequency structure without introducing artifacts. Diffusion based approaches have recently advanced the state of the art in SR. However, most diffusion based SR pipelines operate purely in the spatial domain, which may yield high frequency details that are not well supported by the underlying low resolution evidence. On the other hand, unlike supervised SR models that may inject dataset specific textures, single image SR relies primarily on internal image statistics and can therefore be less prone to dataset-driven hallucinations; nevertheless, ambiguity in the LR observation can still lead to inconsistent high frequency details. To tackle this problem, we introduce BATDiff, an unsupervised Bivariate A trous Wavelet Diffusion model designed to provide structured cross scale guidance during the generative process. BATDiff employs an a Trous wavelet transform that constructs an undecimated multiscale representation in which high frequency components are progressively revealed while the full spatial resolution is preserved. As the core inference mechanism, BATDiff includes a bivariate cross scale module that models parent child dependencies between adjacent scales. It improves high frequency coherence and reduces mismatch artifacts in diffusion based SR. Experiments on standard benchmarks demonstrate that BATDiff produces sharper and more structurally consistent reconstructions than existing diffusion and non diffusion baselines, achieving improvements in fidelity and perceptual quality.
Abstract (translated)
超分辨率（SR）模型的有效性在于其能否在不引入伪影的情况下恢复高频结构。基于扩散的方法最近在超分辨率领域取得了最先进的成果。然而，大多数基于扩散的超分辨率流水线仅在空间域内操作，这可能会产生低分辨率输入数据无法充分支持的高频细节。另一方面，与可能注入特定于数据集纹理的监督学习SR模型不同，单图像SR主要依赖于内部图像统计信息，因此不太容易出现由数据驱动导致的幻觉；不过，低分辨率观察中的模糊性仍然可能导致不一致的高频细节。 为了解决这个问题，我们引入了BATDiff，这是一种无监督的双变量A-trous小波扩散模型，旨在在生成过程中提供结构化的跨尺度指导。BATDiff采用了一个A-trous小波变换，构建了一个未下采样的多尺度表示，在该表示中逐步揭示高频成分的同时保持完整的空间分辨率不变。作为核心推断机制，BATDiff包含一个双变量跨尺度模块，用于建模相邻尺度之间的父代-子代依赖关系。这可以提高高频一致性和减少基于扩散的SR中的不匹配伪影。在标准基准测试上的实验表明，与现有的基于扩散和非扩散的方法相比，BATDiff能够生成更清晰、结构上更加一致的重建图像，在保真度和感知质量方面取得了改进。
URL
https://arxiv.org/abs/2603.07234
