  Image restoration (IR) has been an indispensable and challenging task in the
low-level vision field, which strives to improve the subjective quality of
images distorted by various forms of degradation. Recently, the diffusion model
has achieved significant advancements in the visual generation of AIGC, thereby
raising an intuitive question, "whether diffusion model can boost image
restoration". To answer this, some pioneering studies attempt to integrate
diffusion models into the image restoration task, resulting in superior
performances than previous GAN-based methods. Despite that, a comprehensive and
enlightening survey on diffusion model-based image restoration remains scarce.
In this paper, we are the first to present a comprehensive review of recent
diffusion model-based methods on image restoration, encompassing the learning
paradigm, conditional strategy, framework design, modeling strategy, and
evaluation. Concretely, we first introduce the background of the diffusion
model briefly and then present two prevalent workflows that exploit diffusion
models in image restoration. Subsequently, we classify and emphasize the
innovative designs using diffusion models for both IR and blind/real-world IR,
intending to inspire future development. To evaluate existing methods
thoroughly, we summarize the commonly-used dataset, implementation details, and
evaluation metrics. Additionally, we present the objective comparison for
open-sourced methods across three tasks, including image super-resolution,
deblurring, and inpainting. Ultimately, informed by the limitations in existing
works, we propose five potential and challenging directions for the future
research of diffusion model-based IR, including sampling efficiency, model
compression, distortion simulation and estimation, distortion invariant
learning, and framework design.
