Multiplicative noise removal via sparse and redundant representations over learned dictionaries and total variation

Authors:
Yan Hao;Xiangchu Feng;Jianlou Xu
Affiliations:
School of Science, Xidian University, Xi'an 710071, China;School of Science, Xidian University, Xi'an 710071, China;School of Science, Xidian University, Xi'an 710071, China and School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471003, China
Venue:
Signal Processing
Year:
2012

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Abstract

In this paper, we propose a new three-stage model for multiplicative noise removal. In the first stage, sparse and redundant representation is used to approximate the log-image. The K-SVD algorithm is used to train a redundant dictionary, which can describe the log-image sparsity. Then in the second stage, we use the total variation (TV) method to amend the image obtained. At last, via an exponential function and bias correction, the result is transformed back from the log-domain to the real one. Our method combines the advantages of sparse and redundant representation over trained dictionary and TV method. Experimental results show that the new model is more effective to filter out multiplicative noise than the state-of-the-art models.