@conference {208961,
	title = {Efficient image reconstruction for gigapixel quantum image sensors},
	booktitle = {IEEE Global Conference on Signal and Information Processing (GlobalSIP)},
	year = {2014},
	month = {Dec.},
	address = {Atlanta, GA},
	abstract = {Recent advances in materials, devices and fabrication technologies\&nbsp;have motivated a strong momentum in developing solid-state sensors\&nbsp;that can detect individual photons in space and time. It has been envisioned\&nbsp;that such sensors can eventually achieve very high spatial\&nbsp;resolutions (e.g., 10^9 pixels/chip) as well as high frame rates (e.g.,\&nbsp;10^6 frames/sec). In this paper, we present an efficient algorithm to\&nbsp;reconstruct images from the massive binary bit-streams generated by\&nbsp;these sensors. Based on the concept of alternating direction method\&nbsp;of multipliers (ADMM), we transform the computationally intensive\&nbsp;optimization problem into a sequence of subproblems, each of which\&nbsp;has efficient implementations in the form of polyphase-domain filtering\&nbsp;or pixel-wise nonlinear mappings. Moreover, we reformulate\&nbsp;the original maximum likelihood estimation as maximum a posterior\&nbsp;estimation by introducing a total variation prior. Numerical results\&nbsp;demonstrate the strong performance of the proposed method,\&nbsp;which achieves several dB{\textquoteright}s of improvement in PSNR and requires a\&nbsp;shorter runtime as compared to standard gradient-based approaches.},
	author = {S. H. Chan and Lu, Y.M.}
}
