LEARNING AN ADAPTIVE DICTIONARY USING A PROJECTED GRADIENT METHOD AND ITS APPLICATION ON IMAGE DE-NOISING

摘要

In recent years, considerable efforts have been made in the research of sparse representation for signals over over-complete dictionaries. The dictionaries can be either prespecified transforms or designed by learning from a set of training signals. In the paper, the dictionary learning problem was extended into a quadratic programming framework. A projected gradient with line search method was presented for solving this large-scale box-constrained quadratic program. The non-negative dictionary learned using this method was applied to image de-noising. Experimental results demonstrated that this learning-based method had better performance than the wavelet-based, the variation-based and the K-SVD methods.