The separation of noisy image is a very exciting area of research, especially when no prior information is available about the noisy image. In this paper, we propose a robust independent component analysis (ICA) network for separation images contaminated with high-level additive noise or outliers. We reduce the power of additive noise by adding outlier rejection rule in ICA. Extensive computer simulations confirm robustness and the excellent performance of the resulting algorithms.