Image restoration via multi-prior collaboration

Feng Jiang, Shengping Zhang, Debin Zhao, S. Y. Kung

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper proposes a novel multi-prior collaboration framework for image restoration. Different from traditional non-reference image restoration methods, a big reference image set is adopted to provide the references and predictions of different popular prior models and accordingly further guide the subsequentmulti-prior collaboration. In particular, the collaboration of multi-prior models is mathematically formulated as a ridge regression problem. Due to expensive computation complexity of handling big reference data, scatter-matrix-based kernel ridge regression is proposed, which achieves high accuracy while low complexity. Additionally, an iterative pursuit is further proposed to obtain refined and robust restoration results. Five popular prior methods are applied to evaluate the effectiveness of the proposed multi-prior collaboration framework. Comparedwith the state-of-the-art image restoration approaches, the proposed framework improves the restoration performance significantly.

Original languageEnglish (US)
Pages (from-to)191-204
Number of pages14
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9005
DOIs
StatePublished - 2015
Event12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore
Duration: Nov 1 2014Nov 5 2014

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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