LAUSR

Coded-illumination (CI) imaging is a feasible technique enabling resolution enhancement and high-dimensional information extraction in optical systems. It incorporates optical encoding and computational reconstruction together to help overcome physical limitations. Existing CI reconstruction methods suffer from a trade-off between noise robustness and low computational complexity, which are both requisite for practical applications. In this paper, we propose a novel noise-robust and low-complexity reconstruction scheme for CI imaging. The scheme runs in an iterative way, and each iteration consists of two phases. First, the measurements are input into a novel non-uniform and adaptive weighted solver, whose weight updates in each iteration. This enables effective identification and attenuation of various measurement noise from coarse to fine. Second, the preserved latent information enters an alternating projection optimization procedure, which reconstructs target image by imposing support constraints without matrix lifting. We have successfully applied the scheme to structured illumination imaging and Fourier ptychography. Both simulations and experiments demonstrate that the method obtains strong robustness, low computational complexity, and fast convergence. The scheme can be adopted for various incoherent and coherent CI imaging modalities with wide extensions.