Efficient image reconstruction for gigapixel quantum image sensors


S. H. Chan and Y. M. Lu, “Efficient image reconstruction for gigapixel quantum image sensors,” in IEEE Global Conference on Signal and Information Processing (GlobalSIP), Atlanta, GA, 2014.

Date Presented:



Recent advances in materials, devices and fabrication technologies have motivated a strong momentum in developing solid-state sensors that can detect individual photons in space and time. It has been envisioned that such sensors can eventually achieve very high spatial resolutions (e.g., 10^9 pixels/chip) as well as high frame rates (e.g., 10^6 frames/sec). In this paper, we present an efficient algorithm to reconstruct images from the massive binary bit-streams generated by these sensors. Based on the concept of alternating direction method of multipliers (ADMM), we transform the computationally intensive optimization problem into a sequence of subproblems, each of which has efficient implementations in the form of polyphase-domain filtering or pixel-wise nonlinear mappings. Moreover, we reformulate the original maximum likelihood estimation as maximum a posterior estimation by introducing a total variation prior. Numerical results demonstrate the strong performance of the proposed method, which achieves several dB’s of improvement in PSNR and requires a shorter runtime as compared to standard gradient-based approaches.

Last updated on 12/31/2014