RWRM: Residual Wasserstein regularization model for image restoration

Ruiqiang He, School of Mathematics and Statistics, Xidian University, Xi'an 710126, China, Xiangchu Feng, Xiaolong Zhu, Hua Huang, Bingzhe Wei, Department of Mathematics, Xinzhou Teachers University, Xinzhou 034000, China
2020 Inverse Problems and Imaging  
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
more » ... eme 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. 2020 Mathematics Subject Classification. 68U10.
doi:10.3934/ipi.2020069 fatcat:gekg3ho5qzgrzdr72avio4e3vm