Name
Abstract | ||
|---|---|---|
A quanta image sensor (QIS) is a class of single-photon imaging devices that measure light intensity using oversampled binary observations. Because of the stochastic nature of the photon arrivals, data acquired by QIS is a massive stream of random binary bits. The goal of image reconstruction is to recover the underlying image from these bits. In this paper, we present a non-iterative image reconstruction algorithm for QIS. Unlike existing reconstruction methods that formulate the problem from an optimization perspective, the new algorithm directly recovers the images through a pair of nonlinear transformations and an off-the-shelf image denoising algorithm. By skipping the usual optimization procedure, we achieve orders of magnitude improvement in speed and even better image reconstruction quality. We validate the new algorithm on synthetic datasets, as well as real videos collected by one-bit single-photon avalanche diode (SPAD) cameras. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/s16111961 | SENSORS |
Keywords | Field | DocType |
single-photon image sensor,quanta image sensor (QIS),image reconstruction,quantized Poisson statistics,image denoising,Anscombe Transform,maximum likelihood estimation (MLE) | Iterative reconstruction,Computer vision,Feature detection (computer vision),Image sensor,Anscombe transform,Binary image,Image processing,Artificial intelligence,Engineering,Image restoration,Binary number | Journal |
Volume | Issue | ISSN |
16 | 11.0 | 1424-8220 |
Citations | PageRank | References |
2 | 0.38 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stanley H. Chan | 1 | 403 | 30.95 |
| Omar A. Elgendy | 2 | 64 | 4.68 |
| Xiran Wang | 3 | 51 | 1.63 |