Optical cryptosystem based on phase-truncated-Fourier-transforms (PTFT) is one of the most interesting optical cryptographic schemes due to its unique mechanism of encryption/decryption. Conventional learning-based attack method need a large number of plaintext-ciphertext pairs to train a neural network and then predict the plaintexts from subsequent ciphertexts. In this work, we propose an alternative method of attack on PTFT-based optical asymmetric cryptosystem by using an untrained neural network. We optimize the parameters of a neural network with the help of the encryption model of PTFT-based cryptosystem, hoping to get the ability of retrieving any plaintext from the corresponding unknown ciphertext but without help of the decryption keys. The proposed untrained-neural-network-based attack approach eliminates the requirement of tens of thousands of training images and might open up a new avenue for optical cryptanalysis.
Fourier ptychographic microscopy is a newly developed method to extend the resolution beyond the conventional limit defined by a microscope optics. The positions of the LED sources strongly determine the quality of the reconstructed result. In this paper, we propose a new positional misalignment correction method, which is based on the distribution of the incident LED intensity. When the LED matrix panel has displacements along x-axis, or y-axis, the incident LED intensity distribution which propagates to the sample plane will be changed. An optimization method to correct positional misalignment is introduced, as well as the light intensity correction. Simulation has been performed to verify the effectiveness of the proposed method, which demonstrates that the reconstructed result shows a better quality.