用确定性路径提升超分辨率图像真实感
DeltaDiff: Reality-Driven Diffusion with AnchorResiduals for Faithful SR
- 构建高分辨与低分辨间的确定性映射，替代随机噪声过程
- 残差空间扩散熵降低25%，有效抑制无关噪声
- 适合追求图像真实性的超分辨率任务，尤其在细节还原上表现优
近期，扩散模型在超分辨率任务中的应用面临保真度下降的问题。由于扩散模型固有的随机采样特性，直接应用于超分辨率会导致生成细节偏离高分辨率图像的真实分布。为此，我们提出DeltaDiff，一种新框架，通过约束扩散过程，其核心是建立高分辨率（HR）与低分辨率（LR）之间的确定性映射路径，而非传统扩散模型的随机噪声扰动过程。理论分析表明，与像素空间扩散相比，残差空间的扩散熵降低了25%，有效抑制了无关噪声干扰。实验结果表明，该方法超越现有最先进模型，生成结果具有更高保真度。本工作为扩散模型在图像重建任务中的应用建立了新的低秩约束范式，平衡了随机生成与结构保真性。代码与模型已公开于https://github.com/continueyang/DeltaDiff。
原文摘要 · Abstract (English)
Recently, the transfer application of diffusion models in super-resolu-tion tasks has faced the problem ofdecreased fidelity. Due to the inherent randomsampling characteristics ofdiffusion models, direct application in super-resolu-tion tasks can result in generated details deviating from the true distribution ofhigh-resolution images. To address this, we propose DeltaDiff, a novel frame.work that constrains the difusion process, its essence is to establish a determin-istic mapping path between HR and LR, rather than the random noise disturbanceprocess oftraditional difusion models. Theoretical analysis demonstrates a 25%reduction in diffusion entropy in the residual space compared to pixel-space diffiusion, effectively suppressing irrelevant noise interference. The experimentalresults show that our method surpasses state-of-the-art models and generates re-sults with better fidelity. This work establishes a new low-rank constrained par-adigm for applying diffusion models to image reconstruction tasks, balancingstochastic generation with structural fidelity. Our code and model are publiclyavailable at https://github.com/continueyang/DeltaDiff .
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