Image Bind Source Separation With Multiscale Geometric Analysis

In real life, the image degenerate problem surely exists during using and conserving, for example: blurred, parti-colored, noised and mixed, etc. Those degenerated images can be looked as the mixed images by mutually independent images without mixing performance. The image separation processing is in accordance with the model of blind source separation (BSS). Therefore, BSS technology has a wide application prospects in the field of image processing.Just like current, scholars in different researching fields have put forward novel ideas and algorithms on account of signal processing, for example: image multiscale geometry analysis (MGA). Images have better sparsity in transform domain. Image sparsity has become a new mathematic tool for BSS, and breathes new vitality into BSS research. BSS algorithms become more and more diversified, and shall be applied in real life.Therefore, those research results of image multiscale geometry analysis would be utilized fully in this dissertation. Tightly couple with the hotspot and difficult point of BSS, extensively research multiscale geometry analysis, and focus on image BSS based on the second generation Curvelet sparse representation. Detail research contents as follows:1. In accordance with the general conditions that those received image signals are non-sparse, utilize image multiscale geometry analysis to make image signals sparse. And focus on researching the second generation Curvelet sparse representation. Analyse the decomposition process of Curvelet transform, certify that it can representate image signals most sparse.2. Research on image BSS based on Curvelet sparse representation in determined conditions. BSS can be implemented in sparse domain. The algorithm uses the kurtosis of sub-images as sparsity criterion, then the most sparse sub-imges can be selected. Simulation experiments certify that the algorithm is feasible and effective.3. Research on image BSS initialization problem. According to signals sparsity by Curvelet transform, the mixed matrix can be estimated with C-means cluster analysis, and the estimated value is looked as initial value of BSS algorithm. The initialization algorithm improves separation rate of convergence and separation pricision.4. Research on image BSS based on Curvelet sparse representation in underdetermined circumstance. According to signals sparsity by Curvelet transform, underdetermined-determined union separation algorithm has been put forward. The algorithm constructs an intermediate mixed matrix, and turns the underdetermined problem into determinted problem. Simulation experiments certify that the algorithm has a good separation result.5. Research on noised-image BSS based on Curvelet sparse representation. Construct an adaptive noise operator with mathematical morphology, and pre-process noised mixed images wjth the noise operator, then separate mixed images with traditional BSS algorithm. The novel algorithm promotes BSS suit to noising circumstance.The research results in this diseertation can be applied to image processing field, for example: image enhancement, image denosing, image identification, and image separation, etc.