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.
|Title of host publication||Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Publication status||Published - 25 Mar 2016|
|Event||The 41st IEEE International Conference on Acoustics, Speech and Signal Processing - Shanghai, China|
Duration: 20 Mar 2016 → 25 Mar 2016
|Conference||The 41st IEEE International Conference on Acoustics, Speech and Signal Processing|
|Period||20/03/2016 → 25/03/2016|
Bibliographical notePaper 1522
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- School of Electronics, Electrical Engineering and Computer Science - Emeritus Professor
- Speech, Image and Vision Systems