Simultaneous Fidelity and Regularization Learning for Image Restoration

Dongwei Ren, Wangmeng Zuo, David Zhang, Lei Zhang, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Most existing non-blind restoration methods are based on the assumption that a precise degradation model is known. As the degradation process can only be partially known or inaccurately modeled, images may not be well restored. Rain streak removal and image deconvolution with inaccurate blur kernels are two representative examples of such tasks. For rain streak removal, although an input image can be decomposed into a scene layer and a rain streak layer, there exists no explicit formulation for modeling rain streaks and the composition with scene layer. For blind deconvolution, as estimation error of blur kernel is usually introduced, the subsequent non-blind deconvolution process does not restore the latent image well. In this paper, we propose a principled algorithm within the maximum a posterior framework to tackle image restoration with a partially known or inaccurate degradation model. Specifically, the residual caused by a partially known or inaccurate degradation model is spatially dependent and complexly distributed. With a training set of degraded and ground-truth image pairs, we parameterize and learn the fidelity term for a degradation model in a task-driven manner. Furthermore, the regularization term can also be learned along with the fidelity term, thereby forming a simultaneous fidelity and regularization learning model. Extensive experimental results demonstrate the effectiveness of the proposed model for image deconvolution with inaccurate blur kernels, deconvolution with multiple degradations and rain streak removal.

Original languageEnglish
Article number8753511
Pages (from-to)284-299
Number of pages16
JournalIEEE transactions on pattern analysis and machine intelligence
Volume43
Issue number1
DOIs
Publication statusPublished - 2021 Jan 1

Bibliographical note

Funding Information:
This work is supported in part by National Natural Scientific Foundation of China (NSFC) under grant (61671182 and 61801326), Hong Kong RGC GRF grant (PolyU 152124/ 15E), and US National Science Foundation CAREER Grant No.1149783.

Publisher Copyright:
© 1979-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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