Blind image separation based on attentional generative adversarial network

Mixing signal separation is an important field of image processing. However, traditional blind source separation (BSS) algorithms were proposed to solve this task utilizing multiple signal constraints such as independent, non-Gaussian, low rank, sparsity, temporal continuity etc. What’s more, as a case of ill-conditioned signal mixing, the single-channel blind source separation (SCBSS) is more difficult. Because neural networks have strong adaptability and self-organization capability, neural network methods based on training and learning ideas are favored by researchers. However, most BSS methods based on neural network are limited by small sample sizes. Among various neural network, generative adversarial network (GAN) has emerged as an interesting candidate because it is free from statistical constraints and samples. Therefore, we present a single-channel blind image separation algorithm based on attention mechanism GAN, coined AGAN, which uses an end-to-end manner, and it will have more hopeful prospects in the blind image separation task. The network with feature extraction, as well as edge guidance to data creates a new way to iteratively separate mixing images. The experimental results show that AGAN can effectively separate the source signal in the mixing images compared with the neural egg separation (NES) algorithm, which is a neural network separation algorithm. Compared with the classical blind source separation algorithms, this method has better separation performance.