A Comprehensive Review of Image Restoration Research Based on Diffusion Models

Jun Li, Heran Wang (), Yingjie Li and Haochuan Zhang
Additional contact information
Jun Li: School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China
Heran Wang: School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China
Yingjie Li: School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China
Haochuan Zhang: School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China

Mathematics, 2025, vol. 13, issue 13, 1-37

Abstract: Image restoration is an indispensable and challenging task in computer vision, aiming to enhance the quality of images degraded by various forms of degradation. Diffusion models have achieved remarkable progress in AIGC (Artificial Intelligence Generated Content) image generation, and numerous studies have explored their application in image restoration, achieving performance surpassing that of other methods. This paper provides a comprehensive overview of diffusion models for image restoration, starting with an introduction to the background of diffusion models. It summarizes relevant theories and research in utilizing diffusion models for image restoration in recent years, elaborating on six commonly used methods and their unified paradigm. Based on these six categories, this paper classifies restoration tasks into two main areas: image super-resolution reconstruction and frequency-selective image restoration. The frequency-selective image restoration category includes image deblurring, image inpainting, image deraining, image desnowing, image dehazing, image denoising, and low-light enhancement. For each area, this paper delves into the technical principles and modeling strategies. Furthermore, it analyzes the specific characteristics and contributions of the diffusion models employed in each application category. This paper summarizes commonly used datasets and evaluation metrics for these six applications to facilitate comprehensive evaluation of existing methods. Finally, it concludes by identifying the limitations of current research, outlining challenges, and offering perspectives on future applications.

Keywords: diffusion model; image restoration; image super-resolution reconstruction; image deblurring; image inpainting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/13/2079/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/13/2079/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:13:p:2079-:d:1686064

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().