Deep Mean-Shift Priors for Image Restoration

Arjomand, Siavash; Jin, Meiguang; Favaro, Paolo; Zwicker, Matthias (2017). Deep Mean-Shift Priors for Image Restoration. In: Advances in Neural Information Processing Systems 2017. Long Beach, CA, USA. December 4-9.

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In this paper we introduce a natural image prior that directly represents a Gaussian- smoothed version of the natural image distribution. We include our prior in a formulation of image restoration as a Bayes estimator that also allows us to solve noise-blind image restoration problems. We show that the gradient of our prior corresponds to the mean-shift vector on the natural image distribution. In addition, we learn the mean-shift vector field using denoising autoencoders, and use it in a gradient descent approach to perform Bayes risk minimization. We demonstrate competitive results for noise-blind deblurring, super-resolution, and demosaicing.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF) > Computer Graphics Group (CGG)
08 Faculty of Science > Institute of Computer Science (INF) > Computer Vision Group (CVG)
08 Faculty of Science > Institute of Computer Science (INF)

UniBE Contributor:

Arjomand, Siavash; Jin, Meiguang; Favaro, Paolo and Zwicker, Matthias

Subjects:

000 Computer science, knowledge & systems
500 Science > 510 Mathematics

Language:

English

Submitter:

Xiaochen Wang

Date Deposited:

20 Apr 2018 12:36

Last Modified:

20 Apr 2018 12:36

BORIS DOI:

10.7892/boris.113226

URI:

https://boris.unibe.ch/id/eprint/113226

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