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Variational Deep Image Restoration

Cited 9 time in Web of Science Cited 18 time in Scopus
Authors

Soh, Jae Woong; Cho, Nam Ik

Issue Date
2022-06
Publisher
Institute of Electrical and Electronics Engineers
Citation
IEEE Transactions on Image Processing, Vol.31, pp.4363-4376
Abstract
This paper presents a new variational inference framework for image restoration and a convolutional neural network (CNN) structure that can solve the restoration problems described by the proposed framework. Earlier CNN-based image restoration methods primarily focused on network architecture design or training strategy with non-blind scenarios where the degradation models are known or assumed. For a step closer to real-world applications, CNNs are also blindly trained with the whole dataset, including diverse degradations. However, the conditional distribution of a high-quality image given a diversely degraded one is too complicated to be learned by a single CNN. Therefore, there have also been some methods that provide additional prior information to train a CNN. Unlike previous approaches, we focus more on the objective of restoration based on the Bayesian perspective and how to reformulate the objective. Specifically, our method relaxes the original posterior inference problem to better manageable sub-problems and thus behaves like a divide-and-conquer scheme. As a result, the proposed framework boosts the performance of several restoration problems compared to the previous ones. Specifically, our method delivers state-of-the-art performance on Gaussian denoising, real-world noise reduction, blind image super-resolution, and JPEG compression artifacts reduction. Our code and more details are available on our project page, https://github.com/JWSoh/VDIR.
ISSN
1057-7149
URI
https://hdl.handle.net/10371/185306
DOI
https://doi.org/10.1109/TIP.2022.3183835
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