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 language | English |
---|---|
Title of host publication | Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1194-1198 |
ISBN (Electronic) | 978-1-4799-9988-0 |
DOIs | |
Publication status | Published - 25 Mar 2016 |
Event | IEEE International Conference on Acoustics, Speech, and Signal Processing 2016 - Shanghai, China Duration: 20 Mar 2016 → 25 Mar 2016 |
Conference
Conference | IEEE International Conference on Acoustics, Speech, and Signal Processing 2016 |
---|---|
Abbreviated title | ICASSP 2016 |
Country/Territory | China |
City | Shanghai |
Period | 20/03/2016 → 25/03/2016 |
Bibliographical note
Paper 1522Fingerprint
Dive into the research topics of 'A Largest Matching Area Approach to Image Denoising'. Together they form a unique fingerprint.Profiles
-
Ming Ji
- School of Electronics, Electrical Engineering and Computer Science - Emeritus Professor
- Speech, Image and Vision Systems
Person: Emeritus