While diffusion-based image restoration (IR) methods have achieved remarkable
success, they are still limited by the low inference speed attributed to the
necessity of executing hundreds or even thousands of sampling steps. Existing
acceleration sampling techniques, though seeking to expedite the process,
inevitably sacrifice performance to some extent, resulting in over-blurry
restored outcomes. To address this issue, this study proposes a novel and
efficient diffusion model for IR that significantly reduces the required number
of diffusion steps. Our method avoids the need for post-acceleration during
inference, thereby avoiding the associated performance deterioration.
Specifically, our proposed method establishes a Markov chain that facilitates
the transitions between the high-quality and low-quality images by shifting
their residuals, substantially improving the transition efficiency. A carefully
formulated noise schedule is devised to flexibly control the shifting speed and
the noise strength during the diffusion process. Extensive experimental
evaluations demonstrate that the proposed method achieves superior or
comparable performance to current state-of-the-art methods on three classical
IR tasks, namely image super-resolution, image inpainting, and blind face
restoration, \textit{\textbf{even only with four sampling steps}}. Our code and
model are publicly available at https://github.com/zsyOAOA/ResShift.

通过建立马尔可夫链以提高高质量图像和低质量图像之间转换的效率，并通过精心设计的噪声方案灵活控制扩散过程中的转移速度和噪声强度，本研究提出了一种新的高效扩散模型用于图像恢复，表现优越或可媲美现有的方法，甚至只需进行四步采样。

图像恢复的高效扩散模型：残差位移

Efficient Diffusion Model for Image Restoration by Residual Shifting

Diffusion-based image super-resolution (SR) methods are mainly limited by the
low inference speed due to the requirements of hundreds or even thousands of
sampling steps. Existing acceleration sampling techniques inevitably sacrifice
performance to some extent, leading to over-blurry SR results. To address this
issue, we propose a novel and efficient diffusion model for SR that
significantly reduces the number of diffusion steps, thereby eliminating the
need for post-acceleration during inference and its associated performance
deterioration. Our method constructs a Markov chain that transfers between the
high-resolution image and the low-resolution image by shifting the residual
between them, substantially improving the transition efficiency. Additionally,
an elaborate noise schedule is developed to flexibly control the shifting speed
and the noise strength during the diffusion process. Extensive experiments
demonstrate that the proposed method obtains superior or at least comparable
performance to current state-of-the-art methods on both synthetic and
real-world datasets, even only with 15 sampling steps. Our code and model are
available at this https URL

为解决扩散图像超分辨率方法的低推理速度和性能下降问题，我们提出了一种新颖和高效的扩散模型，通过减少扩散步骤的数量，消除了推理过程中的加速要求以及性能恶化，并通过在高分辨率图像和低分辨率图像之间转移残差来显著提高转换效率。实验显示，该方法在合成和真实数据集上均能获得优秀或至少相当于当前最先进方法的性能，即使只使用 15 个采样步骤。