A Largest Matching Area Approach to Image Denoising

Jack Gaston, Ming Ji, Daniel Crookes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Given the success of patch-based approaches to image denoising,this paper addresses the ill-posed problem of patch size selection.Large patch sizes improve noise robustness in the presence of good matches, but can also lead to artefacts in textured regions due to the rare patch effect; smaller patch sizes reconstruct details more accurately but risk over-fitting to the noise in uniform regions. We propose to jointly optimize each matching patch’s identity and size for gray scale image denoising, and present several implementations.The new approach effectively selects the largest matching areas, subject to the constraints of the available data and noise level, to improve noise robustness. Experiments on standard test images demonstrate our approach’s ability to improve on fixed-size reconstruction, particularly at high noise levels, on smoother image regions.
Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1194-1198
ISBN (Electronic)978-1-4799-9988-0
DOIs
Publication statusPublished - 25 Mar 2016
EventThe 41st IEEE International Conference on Acoustics, Speech and Signal Processing - Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Conference

ConferenceThe 41st IEEE International Conference on Acoustics, Speech and Signal Processing
CountryChina
CityShanghai
Period20/03/201625/03/2016

Bibliographical note

Paper 1522

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  • Cite this

    Gaston, J., Ji, M., & Crookes, D. (2016). A Largest Matching Area Approach to Image Denoising. In Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 1194-1198). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICASSP.2016.7471865