Guohai Situ

TL;DR: A lensless optical security system based on double random-phase encoding in the Fresnel domain is proposed, which can encrypt a primary image to random noise by use of two statistically independent random- phase masks in the input and transform planes, respectively.

TL;DR: This paper relates the deep-learning-inspired solutions to the original computational imaging formulation and use the relationship to derive design insights, principles, and caveats of more general applicability, and explores how the machine learning process is aided by the physics of imaging when ill posedness and uncertainties become particularly severe.

TL;DR: This work introduces the technique of wavelength multiplexing into a double random-phase encoding system to achieve multiple-image encryption and analyzes the minimum separation between two adjacentmultiplexing wavelengths through cross talk and the multiplexed capacity through the correlation coefficient.

TL;DR: An overview of the potential, recent advances, and challenges of optical security and encryption using free space optics is presented, highlighting the need for more specialized hardware and image processing algorithms.

TL;DR: Detailed comparisons between the image reconstructed using deep learning and compressive sensing shows that the proposed GIDL has a much better performance in extremely low sampling rate.