Method for imaging object passing behind scattering medium based on convolutional neural network

The invention discloses a method for imaging an object behind a scattering medium based on a convolutional neural network PDSNet. According to the method, the traditional speckle correlation imaging algorithm principles are combined, the design and optimization of the network are guided, and the limitation of the optical memory effect OME on the imaging field angle FOV is eliminated in a data driving mode. The convolutional neural network PDSNet is a neural network structure suitable for a random scale and a complex target. The hidden object recovery capability of the convolutional neural network PDSNet is experimentally tested, and at least 40 times of optical memory effect range expansion is realized on the premise that the average PSNR is kept above 24dB. And meanwhile, under an untrained scale, the average PSNR of the recovered image is more than 22dB, and complex targets such as a human face are successfully reconstructed. Experimental results given in the invention verify the accuracy and effectiveness