PixRestore：基于像素扩散Transformer的统一图像恢复

摘要

统一图像恢复（UIR）旨在利用单一模型从遭受不同退化的低质量（LQ）图像中恢复出高质量（HQ）内容。近年来，大多数方法采用大规模预训练的文本到图像（T2I）潜在扩散模型，利用其强大的能力和生成先验。然而，潜在T2I模型中的变分自编码器（VAE）可能会丢弃对恢复敏感的细节，而开放式合成先验可能引入与内容不一致的伪影。我们提出PixRestore，一种用于UIR的无VAE像素空间扩散Transformer（DiT），其扩散主干完全从零开始训练，不依赖T2I预训练。PixRestore直接在分块像素上执行流匹配，在保持令牌序列可控的同时保留细粒度细节。为适应不同的退化类型，PixRestore利用LQ-HQ DINO特征相似度来预测层特征的可靠性。来自更可靠层的特征被融合为密集条件注入，而可靠性较低的层则接受更强的HQ特征监督以促进退化消除。我们在包含多样化场景和退化类型的大规模语料库上训练PixRestore，并进一步使用基于DINO的对抗目标将其微调为单步生成器以实现高效推理。在公开基准和真实世界测试集上的实验表明，PixRestore仅需约5000万参数和单步推理，即可在各类UIR模型中实现最佳的整体保真度、感知质量和退化鲁棒性，同时效率远超其他方法。更大规模的PixRestore变体可进一步提升性能，证明了我们像素空间设计的可扩展性。代码和整理的基准测试集可在https://github.com/csslc/PixRestore获取。

English

Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model. Most recent methods adapt large pretrained text-to-image (T2I) latent diffusion models for their strong capacity and generative priors. However, the variational autoencoder (VAE) in latent T2I models may discard restoration-sensitive details, while the open-ended synthesis prior can introduce content-inconsistent artifacts. We present PixRestore, a VAE-free pixel-space Diffusion Transformer (DiT) for UIR, where the diffusion backbone is trained entirely from scratch, without relying on T2I pretraining. PixRestore performs flow matching directly on patchified pixels, preserving fine-grained details while keeping the token sequence tractable. To adapt to different degradations, PixRestore learns to predict the reliability of layer features using LQ--HQ DINO feature similarity. Features from more reliable layers are fused as dense conditioning, while less reliable layers receive stronger HQ-feature supervision to encourage degradation removal. We train PixRestore on a large-scale corpus of diverse scenes and degradations, and further finetune it into a one-step generator using DINO-based adversarial objectives for efficient inference. Experiments on public benchmarks and real-world test sets show that, with only about 50M parameters and single-step inference, PixRestore achieves the best overall fidelity, perceptual quality, and robustness to degradations among competing UIR models while being far more efficient. Larger PixRestore variants can further boost performance, demonstrating the scalability of our pixel-space design. Code and the curated benchmark can be found at https://github.com/csslc/PixRestore.