# Blind Image Separation Method Based on Cascade Generative Adversarial Networks
## 1 Introduction  
Any image that is disturbed or polluted can be regarded as the superposition of two unknown types of source information. For instance, a reflection image can be regarded as the superimposition of a reflection source and the background source and, for the problem of dehazing removal, a ground source is superimposed onto a haze source. Therefore, blind image separation (BIS) techniques are suitable for solving a variety of similar image processing problems and play an important role in image processing tasks [1, 2, 3]. Traditional BIS methods need to obtain partial prior knowledge of the images and they use the prior characteristics of sources such as statistical independence, sparsity, and non-Gaussian distributions to separate images [4, 5, 6]. [PERSON] et al. [7] used a sparse constraint and feedback mechanism to extract image sources. [PERSON] et al. [8] identified a single source point by comparing the absolute direction between the diagonal and horizontal components of the Haar wavelet coefficients of a mixed image. Their method requires that the source have a sufficient sparsity after wavelet transformation. Because most mixed images in practice lack the corresponding prior knowledge, it is difficult to realize separation via the traditional methods, especially in a single-channel scenario. Recently, generative adversarial networks (GANs) attracted substantial attention from researchers because of their strong ability to generate new samples following the statistical characteristics of a training dataset [9, 10, 11, 12]. GANs were successfully applied to BIS. [PERSON] et al. [13] proposed a two-stage, single-image reflection removal algorithm that usedfeature reduction to suppress reflection components. It was combined with the generation of confrontation networks to complete the reconstruction of the background image gradient and separate the background layer from the reflection image. [PERSON] et al. [14] presented the neural egg separation (NES) network, which used generated features to semi-supervise and separate mixed images which had a simple composition, but it was not sophisticated enough to process complex mixed images. [PERSON] et al. [15] introduced a dehazing removal network called multi-scale optimal fusion (MOF), which was an end-to-end convolutional neural network system for dehazing, comprising feature extraction, local extreme values, nonlinear regression, and multi-scale mapping, but it was difficult to use to separate natural images. [PERSON] et al. used a GAN [16] to handle BIS tasks, but the processing images were simple and did not consider multiple application scenarios. These existing methods lack a general solution and, when processing training samples, the problem of accurate sample pairing is ignored. Using remote sensing image dehazing as an example, for the training network, both the haze image and clear image are essential [17], but in actual situations, it is difficult to obtain accurate paired data, and this affects the modeling of image dehazing [18]. Therefore, when considering the problem of image separation, it is necessary to design a universal network that can learn the image mixing model and generate realistic mixed images. In this article, we analyze the characteristics of BIS and build a cascade of GANs for BIS which consists of a UGAN for learning the image mixing and a PAGAN for guiding the image separation. It solves the single-channel BIS problem and applies it to more scenarios. The main contributions of this work can be summarized as follows:  
* A BIS method based on a cascade of GANs including a UGAN and a PAGAN is proposed. The goal of the UGAN is to train a generator that can synthesize new samples following examples of clear images and interference sources. In contrast to the UGAN, the goal of the PAGAN is to train a generator that can separate synthesized images. Moreover, a self-attention module is added to the PAGAN to reduce the difference between the generated image and the ground truth.