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.
翻译:大部分现有的图像恢复模型都是针对具体任务的,不能向不同的降解操作者推广。在这项工作中,我们提议Denoising Difulation Null-Space模型(DNM),这是一个针对任意线性IR问题的新颖零弹式框架,包括但不限于图像超分辨率、彩色化、油漆、压缩感应和分流。DNM只需要事先训练过的现成扩散模型作为基因化模型,无需任何额外的培训或网络修改。通过在反向扩散过程中只精炼空域内容,我们就能产生满足数据一致性和真实性的不同结果。我们进一步提议一个强化和健全的版本,称为DNMM+,支持音响性恢复和改进硬性任务的恢复质量。我们在几项IR任务上的实验表明,DNMM比其他最先进的零弹射光光法方法要强。我们还证明DNM+能够解决复杂的现实应用,例如旧的摄影修复。