Open AccessRWRM: Residual Wasserstein regularization model for image restoration
Author(s) -
Rong He,
Xiaoyi Feng,
Xiaolong Zhu,
Hua Huang,
Bingzhe Wei
Publication year - 2021
Publication title -
inverse problems and imaging
Language(s) - English
Resource type - Journals
eISSN - 1930-8345
pISSN - 1930-8337
DOI - 10.3934/ipi.2020069
Subject(s) - residual , regularization (linguistics) , histogram , image restoration , deconvolution , computer science , gaussian , histogram matching , mathematics , algorithm , mathematical optimization , artificial intelligence , image (mathematics) , image processing , physics , quantum mechanics
Existing image restoration methods mostly make full use of various image prior information. However, they rarely exploit the potential of residual histograms, especially their role as ensemble regularization constraint. In this paper, we propose a residual Wasserstein regularization model (RWRM), in which a residual histogram constraint is subtly embedded into a type of variational minimization problems. Specifically, utilizing the Wasserstein distance from the optimal transport theory, this scheme is achieved by enforcing the observed image residual histogram as close as possible to the reference residual histogram. Furthermore, the RWRM unifies the residual Wasserstein regularization and image prior regularization to improve image restoration performance. The robustness of parameter selection in the RWRM makes the proposed algorithms easier to implement. Finally, extensive experiments have confirmed that our RWRM applied to Gaussian denoising and non-blind deconvolution is effective.