TY - GEN
T1 - A correlation-based dissimilarity measure for noisy patches
AU - Riot, Paul
AU - Almansa, Andrés
AU - Gousseau, Yann
AU - Tupin, Florence
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - In this work, we address the problem of defining a robust patch dissimilarity measure for an image corrupted by an additive white Gaussian noise. The whiteness of the noise, despite being a common assumption that is realistic for RAW images, is hardly used to its full potential by classical denoising methods. In particular, the L2-norm is very widely used to evaluate distances and similarities between images or patches. However, we claim that a better dissimilarity measure can be defined to convey more structural information. We propose to compute the dissimilarity between patches by using the autocorrelation of their difference. In order to illustrate the usefulness of this measure, we perform three experiments. First, this new criterion is used in a similar patch detection task. Then, we use it on the Non Local Means (NLM) denoising method and show that it improves performances by a large margin. Finally, it is applied to the task of no-reference evaluation of denoising results, where it shows interesting visual properties. In all those applications, the autocorrelation improves over the L2-norm.
AB - In this work, we address the problem of defining a robust patch dissimilarity measure for an image corrupted by an additive white Gaussian noise. The whiteness of the noise, despite being a common assumption that is realistic for RAW images, is hardly used to its full potential by classical denoising methods. In particular, the L2-norm is very widely used to evaluate distances and similarities between images or patches. However, we claim that a better dissimilarity measure can be defined to convey more structural information. We propose to compute the dissimilarity between patches by using the autocorrelation of their difference. In order to illustrate the usefulness of this measure, we perform three experiments. First, this new criterion is used in a similar patch detection task. Then, we use it on the Non Local Means (NLM) denoising method and show that it improves performances by a large margin. Finally, it is applied to the task of no-reference evaluation of denoising results, where it shows interesting visual properties. In all those applications, the autocorrelation improves over the L2-norm.
UR - https://www.scopus.com/pages/publications/85019711177
U2 - 10.1007/978-3-319-58771-4_15
DO - 10.1007/978-3-319-58771-4_15
M3 - Conference contribution
AN - SCOPUS:85019711177
SN - 9783319587707
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 184
EP - 195
BT - Scale Space and Variational Methods in Computer Vision - 6th International Conference, SSVM 2017, Proceedings
A2 - Lauze, Francois
A2 - Dong, Yiqiu
A2 - Dahl, Anders Bjorholm
PB - Springer Verlag
T2 - 6th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2017
Y2 - 4 June 2017 through 8 June 2017
ER -