A New Method for Image Noise Removal using Chaos-PSO and Nonlinear ICA

The blind source separation (BSS) algorithms, especially the independent component analysis (ICA) algorithms, have been proven to be effective for the image data processing. The noise signal introduced into the image data can be perfectly eliminated using ICA only under the linear mixture condition. However, the images are always mixed nonlinearly with noise perturbations. The traditional linear ICA algorithm is not capable enough to suppress the noise in this situation. Hence, the nonlinear ICA is proposed to deal with the nonlinear mixtures in this paper. The radial basis function (RBF) neural network based post-nonlinear ICA algorithm has been adopted to remove noise from the original image data. To enhance the RBF-ICA operation, the Chaos-Particle Swarm Optimization (PSO) algorithm has been employed to optimize the RBF neural network to obtain satisfactory nonlinear solution of the nonlinear BBS procedure. A series of experiments have been implemented in this work to validate the efficiency of the proposed method. The Chaos-PSO optimized RBF-ICA model has been compared with other ICA models in the image de-noising processing. The comparative results show that the proposed approach is superior to the non-optimized ICA methods with respect to the image de-noising performance.