# Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement
###### Abstract
According to the principle of speckle formation, the scattering medium recodes the incident light field that carries the target information. The researchers have proposed the implementation of computational imaging through scattering media using machine learning, especially deep-learning methods that do not require complex physical models [17; 18; 19; 20; 21; 22; 23; 24]. For example, [PERSON] et al. demonstrated the feasibility of machine learning in achieving target imaging through scattering media using the support vector regression (SVR) algorithm [17]. [PERSON] et al. used a convolutional neural network based on U-Net architecture to reconstruct handwritten numbers for the task of speckle image recovery through media such as frosted glass [18]. [PERSON] et al. applied an encoder-decoder-based generative adversarial network to an optical imaging system to capture the target phase pattern to reconstruct a target in speckle images [19]. These studies overcame the limitations of traditional methods to achieve target imaging through scattering media with relatively low computational efforts and without changing the optical path. The feasibility of machine learning and deep learning in computational imaging through scattering media without any prior physical knowledge has been demonstrated. Nonetheless, the aforementioned end-to-end computational imaging method effectively recovered images only for targets in the same domain owing to the lack of guidance from prior knowledge. To further improve the generalization of the model and explore the influence of target information in speckle images on imaging, [PERSON] et al. designed a hybrid neural network (HNN) deep-learning model to achieve target reconstruction by retrieving as low as 0.1% of the target information from speckle images [25]. [PERSON] et al. proposed classification before reconstruction to achieve efficient target reconstruction of the models under different scattering media [26]. Furthermore, [PERSON] et al. developed a deep neural network to achieve target imaging through different scattering conditions [27]. Although these researchers improved the generalization of the model, in essence, they are still data driven. Owing to the limitation of the acquisition environment, it is extremely difficult to acquire a large variety of comprehensive and scenario-diverse datasets. Therefore, the exploration of physical models of scattering imaging and overcoming data limitations to achieve imaging through scattering media have become attractive research trends. [PERSON] et al. combined autocorrelation physical models with deep learning to effectively reduce the training datasets and reconstruct the target under the same data domain through scattering media [28]. However, in the practical optical-path acquisition, images are susceptible to interference from external factors and generate large noise terms during the autocorrelation calculation, which affects the quality of speckle-correlation imaging. This issue has raised the question of how to combine physical constraints with deep learning to achieve autocorrelation-based speckle recovery with a low signal-to-noise ratio and effectively suppress background noise terms. To address these key issues, this paper designs a scattering recovery algorithm, autocorrelation-enhanced generative adversarial networks (A-eGAN), for random-scale and complex targets based on prior physical knowledge of autocorrelation to reduce the impact of noise on the autocorrelation results. After loading the speckle autocorrelation image datasets of basic elements as the training set, speckle autocorrelation recoveries of the targets under different data domains were achieved by training the deep-learning model. The experimental results show that the method in this paper can improve the signal-to-noise ratio of the autocorrelation images in the target speckle image datasets under different data domains collected from frosted glass plates with different meshes.