Single-pixel image reconstruction based on block compressive sensing and convolutional neural network

Lau, Stephen L. H.; Lim, Ji Sun; Chong, Edwin K. P.; Wang, Xin

doi:10.1504/ijhm.2023.132303

Cited by 10 publications

(1 citation statement)

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“…These methods can directly take a single image as input and generate a reconstructed 3D model as output. By extracting features more efficiently, the reconstruction effect is improved [ 2 , 3 ]. Nevertheless, this approach does have some drawbacks.…”

Section: Introductionmentioning

confidence: 99%

A Study on Refraction Error Compensation Method for Underwater Spinning Laser Scanning Three-Dimensional Imaging

Zhang,

Wang,

Zhang

et al. 2024

Sensors

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Laser scanning 3D imaging technology, because it can obtain accurate three-dimensional surface data, has been widely used in the search for wrecks and rescue operations, underwater resource development, and other fields. At present, the conventional underwater spinning laser scanning imaging system maintains a relatively fixed light window. However, in low-light situations underwater, the rotation of the scanning device causes some degree of water fluctuation, which warps the light strip data that the system sensor receives about the object’s surface. To solve this problem, this research studies an underwater 3D scanning and imaging system that makes use of a fixed light window and a spinning laser (FWLS). A refraction error compensation algorithm is investigated that is based on the fundamentals of linear laser scanning imaging, and a dynamic refraction mathematical model is established based on the motion of the imaging device. The results of the experiment on error analysis in an optimal underwater environment indicate that the error in reconstructing the radius is decreased by 60% (from 2.5 mm to around 1 mm) when compensating for the measurement data of a standard sphere with a radius of 20 mm. Moreover, the compensated point cloud data exhibit a higher degree of correspondence with the model of the standard spherical point cloud. Furthermore, we examine the impact of physical noise, measurement distance, and partial occlusion of the object on the imaging system inside an authentic underwater setting. This study is a good starting point for looking at the refractive error of an underwater laser scanning imaging system. It also provides to us some ideas for future research on the refractive error of other scanning imaging methods.

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Section: Introductionmentioning

confidence: 99%