Long-Range Imaging through Scattering Media Using Deep Learning - Ying Jin; Cangtao Zhou; Wanjun Dai (Photonics)
Imaging through scattering media is an important and challenging problem, and the technology has been used in many fields, such as autonomous driving, industrial inspections, remote sensing imaging, and biomedical imaging. However, most of the previous experiments used numbers or letters for close-range imaging, while objects in life are colorful. In this study, a new deep learning network, DesUNet, was constructed to image realistic objects at medium and long distances under sunlight through scattering media, and to realize object recognition. In addition, this study also compares the imaging results of different neural networks, and the results show that the DesUNet network improves the feature information storage ability and enhances the image reconstruction. It not only clearly restores the original appearance of the object, but also extracts the physical information about the object. In order to further verify the power of the DesUNet network, this study also conducted indoor near distance and outdoor medium distance imaging experiments. For indoor reconstructed objects, the appearance of the objects could be clearly identified. For outdoor reconstructed objects, the confidence level could reach above 0.9 through YOLO. The experiments show that the DesUNet network has good robustness and generalization.