Advances and perspective on morphological component analysis based on sparse representation

The separation of signal and image content into semantic parts plays a key role in applications such as analysis, enhancement, compression, restoration, and more. Although many approaches have been proposed to tackle this problem in recent years, they have many disadvantages. Morphological Component Analysis (MCA) is a novel decomposition method based on sparse representation of signals and images. The main idea of MCA is to decompose a signal or image into its building blocks considering that there is morphological diversity among a signal or an image's components, which can be sparsely represented by different dictionaries. This paper introduces the theory of Morphological Component Analysis. Also, it describes the advances on morphological component analysis. Finally, several main problems have been pointed out and further research directions have been anticipated.