title: A weighted dictionary learning model for denoising images corrupted by mixed noise.
abstract: This paper proposes a general weighted  l(2)-l(0) norms energy minimization model to remove mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse noise from the images. The approach is built upon maximum likelihood estimation framework and sparse representations over a trained dictionary. Rather than optimizing the likelihood functional derived from a mixture distribution, we present a new weighting data fidelity function, which has the same minimizer as the original likelihood functional but is much easier to optimize. The weighting function in the model can be determined by the algorithm itself, and it plays a role of noise detection in terms of the different estimated noise parameters. By incorporating the sparse regularization of small image patches, the proposed method can efficiently remove a variety of mixed or single noise while preserving the image textures well. In addition, a modified K-SVD algorithm is designed to address the weighted rank-one approximation. The experimental results demonstrate its better performance compared with some existing methods.
authors.affiliation: School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, Ministry of Education, Beijing Normal University, Beijing 100875, China. jliu@bnu.edu.cn
authors.forename: Jun
authors.initials: J
authors.lastname: Liu
authors.forename: Xue-Cheng
authors.initials: XC
authors.lastname: Tai
authors.forename: Haiyang
authors.initials: H
authors.lastname: Huang
authors.forename: Zhongdan
authors.initials: Z
authors.lastname: Huan
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D004014:Dictionaries as Topic; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio