Reconstructing images of two adjacent objects passing through scattering medium via deep learning

Abstract

In this paper, to the best of our knowledge, we first present a deep learning based method for reconstructing the images of two adjacent objects passing through scattering media. We construct an imaging system for imaging of two adjacent objects located at different depths behind the scattering medium. In general, as the light field of two adjacent objects passes through the scattering medium, a speckle pattern is obtained. We employ the designed adversarial network, which is called as YGAN, for reconstructing the two images simultaneously from the speckle. It is shown that based on the trained YGAN, we can reconstruct images of the two adjacent objects with high quality. In addition, the influence of object image types, and the location depths of the two adjacent objects on the imaging fidelity will be studied. Results demonstrate the strong generalization ability and effectiveness of the YGAN. Even in the case where another scattering medium is inserted between the two objects, the YGAN can reconstruct the object images with high fidelity. The technique presented in this paper can be used for applications in areas of medical image analysis, such as medical image classification, segmentation, and studies of multi-object scattering imaging, three-dimensional imaging etc.