Transductive Gaussian processes for image denoising

Shenlong Wang, Lei Zhang, Raquel Urtasun

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

Abstract

In this paper we are interested in exploiting self-similarity information for discriminative image denoising. Towards this goal, we propose a simple yet powerful denoising method based on transductive Gaussian processes, which introduces self-similarity in the prediction stage. Our approach allows to build a rich similarity measure by learning hyper parameters defining multi-kernel combinations. We introduce perceptual-driven kernels to capture pixel-wise, gradient-based and local-structure similarities. In addition, our algorithm can integrate several initial estimates as input features to boost performance even further. We demonstrate the effectiveness of our approach on several benchmarks. The experiments show that our proposed denoising algorithm has better performance than competing discriminative denoising methods, and achieves competitive result with respect to the state-of-the-art.
Original languageEnglish
Title of host publication2014 IEEE International Conference on Computational Photography, ICCP 2014
PublisherIEEE Computer Society
ISBN (Print)9781479951888
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 6th IEEE International Conference on Computational Photography, ICCP 2014 - Santa Clara, CA, United States
Duration: 2 May 20144 May 2014

Conference

Conference2014 6th IEEE International Conference on Computational Photography, ICCP 2014
CountryUnited States
CitySanta Clara, CA
Period2/05/144/05/14

ASJC Scopus subject areas

  • Mathematics (miscellaneous)

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