TY - GEN
T1 - Image noise filter based on DCT and fast clustering
AU - de Jesús Martínez Felipe, Miguel
AU - Felipe Riveron, Edgardo M.
AU - Ramirez, Pablo Manrique
AU - Pogrebnyak, Oleksiy
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - An algorithm for filtering images contaminated by additive white Gaussian noise in discrete cosine transform domain is proposed. The algorithm uses a clustering stage to obtain mean power spectrum of each cluster. The groups of clusters are found by the proposed fast algorithm based on 2D histograms and watershed transform. In addition to the mean spectrum of each cluster, the local groups of similar patches are found to obtain the local spectrum, and therefore, derive the local Wiener filter frequency response better and perform the collaborative filtering over the groups of patches. The obtained filtering results are compared to the state-of-the-art filters in terms of peak signal-to-noise ratio and structural similarity index. It is shown that the proposed algorithm is competitive in terms of signal-to-noise ratio and in almost all cases is superior to the state-of-the art filters in terms of structural similarity.
AB - An algorithm for filtering images contaminated by additive white Gaussian noise in discrete cosine transform domain is proposed. The algorithm uses a clustering stage to obtain mean power spectrum of each cluster. The groups of clusters are found by the proposed fast algorithm based on 2D histograms and watershed transform. In addition to the mean spectrum of each cluster, the local groups of similar patches are found to obtain the local spectrum, and therefore, derive the local Wiener filter frequency response better and perform the collaborative filtering over the groups of patches. The obtained filtering results are compared to the state-of-the-art filters in terms of peak signal-to-noise ratio and structural similarity index. It is shown that the proposed algorithm is competitive in terms of signal-to-noise ratio and in almost all cases is superior to the state-of-the art filters in terms of structural similarity.
KW - Collaborative filtering
KW - Fast image clustering
KW - Noise suppression
UR - http://www.scopus.com/inward/record.url?scp=85021209875&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-59226-8_15
DO - 10.1007/978-3-319-59226-8_15
M3 - Contribución a la conferencia
SN - 9783319592251
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 149
EP - 158
BT - Pattern Recognition - 9th Mexican Conference, MCPR 2017, Proceedings
A2 - Carrasco-Ochoa, Jesus Ariel
A2 - Martinez-Trinidad, Jose Francisco
A2 - Olvera-Lopez, Jose Arturo
PB - Springer Verlag
T2 - 9th Mexican Conference on Pattern Recognition, MCPR 2017
Y2 - 21 June 2017 through 24 June 2017
ER -