An Efficient Parallel Algorithm for Single-Pixel and FPA Imaging

摘要

Long image acquisition time is a critical problem in single-pixel-imaging. Here, we propose a new high-speed single-pixel compressive imaging method. We develop an ADMM based optimization algorithm to handle images with multiple features. The proposed method solves an optimization problem with the objectives of Total-Variation and ℓ_1-norm with a data-fidelity constraint. The algorithm is highly parallel and is suitable for implementation using GPUs, with a significant reduction in computation. The resulting system produces high resolution images and can also be used for super-resolution by changing the single detector with a focal plane array. We verify the system experimentally and compare the performance of our algorithm with similar methods.