Separation Of Image Based On Sparse Representation

Blind separation of images is an important task in image processing, and it has been a hot topic in this field nowadays. Blind separation of images is to estimate the unknown sources by using the observed images only from the transmission system, in which one knows just a little prior information of the transmission way, or even knows nothing at all.The main idea of the Morphological Composition Analysis (MCA) and the Sparse Representation (SR) based blind source separation method is to sparsely represent an image under different dictionaries according to the morphological diversity of an image's components. Firstly, the basic theory of sparse representation theory is discussed, which includes the sparse representation model, the design of over-complete dictionary, and the sparse decomposition algorithms. The application of the sparse representation in multi-channel morphological composition analysis is also discussed. Secondly, multi-component dictionary is constructed according to different morphology features of images. We also use multi-component dictionaries to represent an image, and then obtain the sparse representation of the multi-morphology sources. Because of lack of prior information of the source images, the sparse measurement by using the traditional norm can not separate the different components from mixed images. In order to solve this problem, we use the structural norm as the sparse measurement. Finally, a novel iterative algorithm to solve our model is designed. Numerical results show that the proposed model and algorithm is efficient. L1...