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JPEG Artifact Correction using Denoising Diffusion Restoration Models
Bahjat Kawar · Jiaming Song · Stefano Ermon · Michael Elad

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in NeurIPS 2022 Workshop on Score-Based Methods »
Event URL: https://openreview.net/forum?id=O3WJOt79289 »

Diffusion models can be used as learned priors for solving various inverse problems. However, most existing approaches are restricted to linear inverse problems, limiting their applicability to more general cases. In this paper, we build upon Denoising Diffusion Restoration Models (DDRM) and propose a method for solving some non-linear inverse problems. We leverage the pseudo-inverse operator used in DDRM and generalize this concept for other measurement operators, which allows us to use pre-trained unconditional diffusion models for applications such as JPEG artifact correction. We empirically demonstrate the effectiveness of our approach across various quality factors, attaining performance levels that are on par with state-of-the-art methods trained specifically for the JPEG restoration task.

Author Information

Bahjat Kawar (Technion)
Jiaming Song (Stanford University)

I am a first year Ph.D. student in Stanford University. I think about problems in machine learning and deep learning under the supervision of Stefano Ermon. I did my undergrad at Tsinghua University, where I was lucky enough to collaborate with Jun Zhu and Lawrence Carin on scalable Bayesian machine learning.

Stefano Ermon (Stanford)
Michael Elad (Technion)

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