Paper Info
Title
Image Filter Based on Block Matching, Discrete Cosine Transform and Principal Component Analysis
Abstract
An algorithm for filtering the images contaminated by additive white Gaussian noise is proposed. The algorithm uses the groups of Hadamard transformed patches of discrete cosine coefficients to reject noisy components according to Wiener filtering approach. The groups of patches are found by the proposed block similarity search algorithm of reduced complexity performed on block patches in transform domain. When the noise variance is small, the proposed filter uses an additional stage based on principal component analysis; otherwise the experimental Wiener filtering is performed. The obtained filtering results are compared to the state of the art filters in terms of peak signal-to-noise ratio and structure similarity index. It is shown that the proposed algorithm is competitive in terms of signal to noise ratio and almost in all cases is superior to the state of the art filters in terms of structure similarity.
Year
DOI
Venue
2016
10.1007/978-3-319-62434-1_34
ADVANCES IN COMPUTATIONAL INTELLIGENCE, MICAI 2016, PT I
Keywords
Field
DocType
Image filtering,Principal component analysis,Block matching
Wiener filter,Pattern recognition,Lapped transform,Modified discrete cosine transform,Computer science,Discrete cosine transform,Signal-to-noise ratio,Transform coding,Filter (signal processing),Artificial intelligence,Discrete sine transform
Conference
Volume
ISSN
Citations 
10061
0302-9743
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Alejandro I. Callejas Ramos100.34
Edgardo M. Felipe-Riverón25212.61
Pablo Ramírez314.74
Oleksiy Pogrebnyak42810.20