期刊
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
卷 1, 期 2, 页码 112-125出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCI.2015.2453093
关键词
3-D imaging; computational imaging; convex optimization; first-photon imaging; LIDAR ,low-light imaging; Poisson noise; single-photon detection; time-of-flight imaging
资金
- U.S. National Science Foundation [1161413, 1422034]
- Samsung Scholarship
- Microsoft Ph.D. Fellowship
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1422034] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1161413] Funding Source: National Science Foundation
Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with detectors sensitive to individual photons, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We develop a robust method for estimating depth and reflectivity using fixed dwell time per pixel and on the order of one detected photon per pixel averaged over the scene. Our computational image formation method combines physically accurate single-photon counting statistics with exploitation of the spatial correlations present in real-world reflectivity and 3-D structure. Experiments conducted in the presence of strong background light demonstrate that our method is able to accurately recover scene depth and reflectivity, while traditional imaging methods based on maximum likelihood (ML) estimation or approximations thereof lead to noisier images. For depth, performance compares favorably to signal-independent noise removal algorithms such as median filtering or block-matching and 3-D filtering (BM3D) applied to the pixelwise ML estimate; for reflectivity, performance is similar to signal-dependent noise removal algorithms such as Poisson nonlocal sparse PCA and BM3D with variance-stabilizing transformation. Our framework increases photon efficiency 100-fold over traditional processing and also improves, somewhat, upon first-photon imaging under a total acquisition time constraint in raster-scanned operation. Thus, our new imager will be useful for rapid, low-power, and noise-tolerant active optical imaging, and its fixed dwell time will facilitate parallelization through use of a detector array.
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