论文标题
使用DENOISISER扩散空间模型的零拍图像恢复
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
论文作者
论文摘要
大多数现有的图像恢复（IR）模型是特定于任务的，无法推广到不同的退化操作员。在这项工作中，我们提出了denoising扩散零空间模型（DDNM），这是一个新型的零摄像机框架，用于任意线性IR问题，包括但不限于图像超分辨率，着色，inpherting，Inpainting，inpainting，inpainting，inpainting，inpainting，inpainting，inpainting，inpainting，inpainting，inpainting，inpainting，inpainting，inpherting，inpainting，inpherting，inpherting，inpherting，inpherting，inpherting，inpherting，inpherting，inpherting，inpherting，“压缩感应”和“ deblurring”中。 DDNM仅需要预先训练的现成扩散模型作为生成性先验，而没有任何额外的培训或网络修改。通过仅在反向扩散过程中完善空间含量，我们可以产生各种结果，以满足数据一致性和现实性。我们进一步提出了一个增强和健壮的版本，称为DDNM+，以支持嘈杂的恢复并提高艰苦任务的恢复质量。我们对几个IR任务的实验表明，DDNM的表现优于其他最先进的IR方法。我们还证明了DDNM+可以解决复杂的现实世界应用，例如旧照片修复。
Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators. In this work, we propose the Denoising Diffusion Null-Space Model (DDNM), a novel zero-shot framework for arbitrary linear IR problems, including but not limited to image super-resolution, colorization, inpainting, compressed sensing, and deblurring. DDNM only needs a pre-trained off-the-shelf diffusion model as the generative prior, without any extra training or network modifications. By refining only the null-space contents during the reverse diffusion process, we can yield diverse results satisfying both data consistency and realness. We further propose an enhanced and robust version, dubbed DDNM+, to support noisy restoration and improve restoration quality for hard tasks. Our experiments on several IR tasks reveal that DDNM outperforms other state-of-the-art zero-shot IR methods. We also demonstrate that DDNM+ can solve complex real-world applications, e.g., old photo restoration.
