Taming Diffusion Models for Image Restoration: A Review
<|reference_start|>Taming Diffusion Models for Image Restoration: A Review: Diffusion models have achieved remarkable progress in generative modelling, particularly in enhancing image quality to conform to human preferences. Recently, these models have also been applied to low-level computer vision for photo-realistic image restoration (IR) in tasks such as image denoising, deblurring, dehazing, etc. In this review paper, we introduce key constructions in diffusion models and survey contemporary techniques that make use of diffusion models in solving general IR tasks. Furthermore, we point out the main challenges and limitations of existing diffusion-based IR frameworks and provide potential directions for future work.<|reference_end|>
@article{luo2024taming,
    title={Taming Diffusion Models for Image Restoration: A Review},
    author={Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sj"olund, Thomas
  B. Sch"on},
    journal={arXiv preprint arXiv:2409.10353},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10353},
    primaryClass={cs.CV}
}