Image reconstruction through dynamic scattering media based on deep learning

Abstract

Under complex scattering conditions, it is very difficult to capture clear object images hidden behind the media by modelling the inverse problem. With regard to dynamic scattering media, the challenge increases. For solving the inverse problem, we propose a new class-specific image reconstruction algorithm. The method based on deep learning classifies blurred scattering images according to scattering conditions and then recovers to clear images hidden behind the media. The deep learning network is used to learn the mapping relationship between the object and the scattering image rather than characterizing the scattering media explicitly or parametrically. 25000 scattering images are obtained under five sets of dynamic scattering condition to verify the feasibility of the proposed method. In addition, the generalizability of the method has been verified successfully. Compared with common CNN method, it's confirmed that our algorithm has better performance in reconstructing higher-quality images. Furthermore, for a given scattering image with unknown scattering condition, the closest scattering condition information can be given by classification network, and then the corresponding clear image is restored by reconstruction network.