@inproceedings{4a0e6b3260da404c99976c923aeb9347,
title = "Towards a Bayesian Video denoising method",
abstract = "The quality provided by image and video sensors increases steadily, and for a fixed spatial resolution the sensor noise has been gradually reduced over the years. However, modern sensors are also capable of acquiring at higher spatial resolutions which are still affected by noise, specially under low lighting conditions. The situation is even worse in video cameras, where the capture time is bounded by the frame rate. The noise in the video degrades its visual quality and hinders its analysis. In this paper we present a new video denoising method extending the non-local Bayes image denoising algorithm. The method does not require motion estimation, and yet preliminary results show that it compares favourably with the state-of-the-art methods in terms of PSNR.",
keywords = "Bayesian methods, Patch-based methods, Video denoising",
author = "Pablo ARIAS and Jean-Michel MOREL",
year = "2015",
doi = "10.1007/978-3-319-25903-1\_10",
language = "English",
isbn = "9783319259024",
series = "Lecture Notes in Computer Science",
publisher = "Springer, Cham",
pages = "107--117",
editor = "Sebastiano BATTIATO and Jacques BLANC-TALON and Giovanni GALLO and Wilfried PHILIPS and Dan POPESCU and Paul SCHEUNDERS",
booktitle = "Advanced Concepts for Intelligent Vision Systems, 16th International Conference, ACIVS 2015. Proceedings",
note = "16th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2015 ; Conference date: 26-10-2015 Through 29-10-2015",
}