Paper Info
Title
Efficient image reconstruction for gigapixel quantum image sensors
Abstract
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., 109 pixels/chip) as well as high frame rates (e.g., 106 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.
Year
DOI
Venue
2014
10.1109/GlobalSIP.2014.7032129
Signal and Information Processing
Keywords
Field
DocType
image reconstruction,image sensors,maximum likelihood estimation,optimisation,ADMM,alternating direction method of multipliers,gigapixel quantum image sensor,image reconstruction,maximum a posterior estimation,maximum likelihood estimation,optimization problem,ADMM,Image reconstruction,gigapixel imaging,quantum image sensors
Iterative reconstruction,Computer vision,Image sensor,Filter (signal processing),Algorithm,Chip,Frame rate,Artificial intelligence,Pixel,Optimization problem,Mathematics,Binary number
Conference
Citations 
PageRank 
References 
2
0.40
7
Authors
2
Name
Order
Citations
PageRank
Stanley H. Chan140330.95
Yue M. Lu267760.17