Re-weighting the morphological diversity
Signal separation has a fundamental role in many image applications, such as noise removing (white noise, reflection, rain, etc), segmentation, and inpainting. To fulfill signal separation, morphological component analysis (MCA) has been widely deployed in a plenty of applications [1], [2], [3]. MCA uses dictionaries to model morphologies of subcomponents, but the coherence between dictionaries may cause the defect presenting in the obtained subcomponents [4], [5]. In this article, we replace the sparse coding of MCA with the weighted sparse coding, and by assigning heavier weights to dictionaries' highly coherent atoms, the defect presenting in the obtained subcomponents is reduced. The experimental results show that the proposed signal separation algorithm achieves a significant performance gain over MCA.