TY - GEN
T1 - Non local image denoising using image adapted neighborhoods
AU - Pardo, Álvaro
PY - 2010
Y1 - 2010
N2 - In recent years several non-local image denoising methods were proposed in the literature. These methods compute the denoised image as a weighted average of pixels across the whole image (in practice across a large area around the pixel to be denoised). The algorithm non-local means (NLM) proposed by Buades, Morel and Coll showed excellent denoising capabilities. In this case the weight between pixels is based on the similarity between square neighborhoods around them. NLM was a clear breakthrough when it was proposed but then was outperformed by algorithms such as BM3D. The improvements of these algorithms are very clear with respect to NLM but the reasons for such differences are not completely understood. One of the differences between both algorithms is that they use adaptive regions to compute the denoised image. In this article we will study the performance of NLM while using image adapted neighborhoods.
AB - In recent years several non-local image denoising methods were proposed in the literature. These methods compute the denoised image as a weighted average of pixels across the whole image (in practice across a large area around the pixel to be denoised). The algorithm non-local means (NLM) proposed by Buades, Morel and Coll showed excellent denoising capabilities. In this case the weight between pixels is based on the similarity between square neighborhoods around them. NLM was a clear breakthrough when it was proposed but then was outperformed by algorithms such as BM3D. The improvements of these algorithms are very clear with respect to NLM but the reasons for such differences are not completely understood. One of the differences between both algorithms is that they use adaptive regions to compute the denoised image. In this article we will study the performance of NLM while using image adapted neighborhoods.
UR - http://www.scopus.com/inward/record.url?scp=78649914952&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-16687-7_39
DO - 10.1007/978-3-642-16687-7_39
M3 - Contribución a la conferencia
AN - SCOPUS:78649914952
SN - 3642166865
SN - 9783642166860
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 277
EP - 284
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 15th Iberoamerican Congress on Pattern Recognition, CIARP 2010, Proceedings
T2 - 15th Iberoamerican Congress on Pattern Recognition, CIARP 2010
Y2 - 8 November 2010 through 11 November 2010
ER -