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Yile Xiao

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Proceedings Article | 4 December 2024 Paper

Fast fringe image denoising for 3D reconstruction using lightweight convolutional neural network

Linyue Zhou, Yixuan Li, Mingzheng Li, Yile Xiao, Shijie Feng

Proceedings Volume 13283, 1328343 (2024) https://doi.org/10.1117/12.3037201

KEYWORDS: Denoising, 3D modeling, Infrared radiation, Cameras, 3D metrology, Infrared imaging, Microelectromechanical systems, Image quality, Infrared cameras, Projection systems

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Fringe projection profilometry (FPP) is widely used in the field of three-dimensional (3D) measurement, owing to its advantages of simple hardware configuration, high accuracy and speed. The traditional fringe projection systems mainly use visible light as a projection source, but they are not suitable for the measurement of human faces and shadow objects. Whereas infrared micro electro mechanical system (MEMS) project invisible infrared light and is more suitable for use as an illumination system for 3D measurement. However, the infrared fringe pattern generated by the MEMS projector usually carries a large amount of speckle noise, which leads to a limitation in the quality of 3D reconstruction. Therefore, in this paper, a lightweight convolutional neural network framework is proposed to achieve fast and high-precision denoising of infrared fringe images. A three-step phase shift method and a multi-view stereo phase matching method are used to compute the absolute phase for the denoised fringe images, achieving fast high-precision 3D reconstruction. Experiments are conducted in static and dynamic scenes, verifying that the lightweight network can improve the denoising speed while guaranteeing similar accuracy to conventional methods, while the designed system is capable of fast high-precision 3D reconstruction at a speed of more than 25 frames per second and an accuracy of 80µm.

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