Adaptive image decomposition via dictionary learning with stuctural incoherence

Initialization sensitivity usually occurs in dictionary learning algorithm for image decomposition. In this paper, we propose an adaptive dictionary learning algorithm by promoting structural incoherence at the stage of dictionary updating. The structural incoherence based dictionary learning (SIDL) method guides the cartoon and texture parts to be more properly represented by two incoherent dictionaries. The resulting minimization is approximately addressed by majorization-minimization (MM) technique. Experimental results demonstrate that the dictionaries generated by SIDL can better describe different morphological contents and subsequently the cartoon and texture components are better separated, in terms of visual comparisons and quantitative measures.