Convolutional Blind Source Separation Implementation

This program implements self-developed convolutional blind source separation primarily through function calls. Specifically, the program first performs image convolution operations, then applies blind separation techniques, and finally outputs the processed image. Although concise in structure, this implementation successfully accomplishes the complex task of blind source separation, providing significant reference value for image processing research. The implementation involves three key stages: 1) Convolution operation handling using matrix-based filtering functions 2) Blind separation algorithm application through iterative optimization methods 3) Result visualization using image output routines Key functions include convolution matrix computation, separation parameter estimation, and result validation modules. The program's efficiency lies in its modular design where each function handles specific subtasks while maintaining data consistency throughout the processing pipeline.