Department of Mathematics
Multiplicative noise removal via a learned dictionary
Multiplicative noise removal is a challenging image processing problem, and most existing methods are based on the maximum a posteriori formulation and the logarithmic transformation of multiplicative denoising problems into additive denoising problems. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, in this paper, we propose to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods. © 2012 IEEE.
Denoising, dictionary, multiplicative noise, sparse representation, variational model
Source Publication Title
IEEE Transactions on Image Processing
Institute of Electrical and Electronics Engineers
Link to Publisher's Edition
Huang, Yu-Mei, Lionel Moisan, Michael K. Ng, and Tieyong Zeng. "Multiplicative noise removal via a learned dictionary." IEEE Transactions on Image Processing 21.11 (2012): 4534-4543.