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

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.

► We give a new model for multiplicative noise removal. ► We give the new algorithm for our model. ► Our method might be preferable over other methods at present. ► Experimental results show that our algorithm is simple and efficient for multiplicative noise removal.