INDIGO+: a unified INN-guided probabilistic diffusion algorithm for blind and non-blind image restoration
Author(s)
You, Di
Dragotti, Pier Luigi
Type
Journal Article
Abstract
Generative diffusion models are becoming one of the most popular prior in image restoration (IR) tasks due to their remarkable ability to generate realistic natural images. Despite achieving satisfactory results, IR methods based on diffusion models present several limitations. First of all, most non-blind approaches require an analytical expression of the degradation model to guide the sampling process. Secondly, most existing blind approaches rely on families of pre-defined degradation models for training their deep networks. The above issues limit the flexibility of these approaches and so their ability to handle real-world degradation tasks. In this paper, we propose a novel INN-guided probabilistic diffusion algorithm for non-blind and blind image restoration, namely INDIGO and BlindINDIGO, which combines the merits of the perfect reconstruction property of invertible neural networks (INN) with the strong generative capabilities of pre-trained diffusion models. Specifically, we train the forward process of the INN to simulate an arbitrary degradation process and use the inverse to obtain an intermediate image that we use to guide the reverse diffusion sampling process through a gradient step. We also introduce an initialization strategy, to further improve the performance and inference speed of our algorithm. Experiments demonstrate that our algorithm obtains competitive results compared with recently leading methods both quantitatively and visually on synthetic and real-world low-quality images.
Date Issued
2024-09-09
Date Acceptance
2024-08-26
Citation
IEEE Journal of Selected Topics in Signal Processing, 2024, 18 (6), pp.1108-1122
ISSN
1932-4553
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
1108
End Page
1122
Journal / Book Title
IEEE Journal of Selected Topics in Signal Processing
Volume
18
Issue
6
Copyright Statement
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
http://dx.doi.org/10.1109/jstsp.2024.3454957
Subjects
Blind image restoration
diffusion models
image restoration
invertible neural networks
Publication Status
Published
Date Publish Online
2024-09-09