@inproceedings{eb0b769afa964b829052118e68424ad0,
title = "Probabilistic depth-guided multi-view image denoising",
abstract = "A novel probabilistic depth-guided multi-view denoising (PDMD) algorithm is proposed in this work. We formulate the multi-view image denoising problem by considering the uncertainties in depth estimates in noisy environments. Specifically, we employ the geometric distributions of nonlocal neighbors, as well as the block similarities, to approximate the probabilities of depth estimates. We then use those probabilities to average all nonlocal neighbors and perform the minimum mean square error (MMSE) denoising. Simulation results show that the proposed PDMD algorithm provides better denoising performance than conventional algorithms.",
keywords = "Image denoising, depth estimation, multi-view image denoising, nonlocal means filter",
author = "Chul Lee and Chang-Su Kim and Lee, {Sang Uk}",
year = "2013",
doi = "10.1109/ICIP.2013.6738187",
language = "English",
isbn = "9781479923410",
series = "2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings",
publisher = "IEEE Computer Society",
pages = "905--908",
booktitle = "2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings",
note = "2013 20th IEEE International Conference on Image Processing, ICIP 2013 ; Conference date: 15-09-2013 Through 18-09-2013",
}