Research on Single-channel Blind Deconvolution Algorithm for Multi-source Signals

Traditional single-channel blind deconvolution method has the limitation that it can only separate two sources from a mixture. Considering this problem, a Single-Channel Blind Deconvolution algorithm based on optimized deep Convolutional generative adversarial networks (SCBDC) is proposed to separate and deconvolve more than three independent sources and mixing matrix only from a mixture. The experiment is shown on the occlusion Chinese character image datasets, four sources are randomly selected to be mixed with mixing matrix. Peak Signal to Noise Ratio (PSNR) and signal correlation index are combined to evaluate the separation effect. The result shows that the multiple sources can be effectively separated and deconvolved.