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.

本文提出了一种名为 DDNM 的新颖的零射击框架，用于任意线性图像修复问题，包括但不限于图像超分辨率、上色、修补、压缩感知和去模糊。DDNM 只需要一个预先训练好的 diffusion 模型作为生成先验，而不需要任何额外的训练或网络修改。我们在几个 IR 任务上的实验表明，DDNM 优于其他最先进的零射击 IR 方法。