TY - CHAP
T1 - Towards a Bayesian Video denoising method
AU - Arias, Pablo
AU - Morel, Jean-Michel
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2015
Y1 - 2015
N2 - 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. © Springer International Publishing Switzerland 2015.
AB - 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. © Springer International Publishing Switzerland 2015.
KW - Bayesian methods
KW - Patch-based methods
KW - Video denoising
UR - https://www.scopus.com/pages/publications/84949193084
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84949193084&origin=recordpage
U2 - 10.1007/978-3-319-25903-1_10
DO - 10.1007/978-3-319-25903-1_10
M3 - RGC 12 - Chapter in an edited book (Author)
VL - 9386
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 107
EP - 117
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -