Deep-learning-based parallel single-pixel imaging for efficient 3D shape measurement in the presence of strong interreflections by using sampling Fourier strategy

Abstract

Strong interreflections cause intractable systematic errors to traditional 3D shaping methods, for example, fringe projection profilometry. Parallel single-pixel imaging (PSI) captures the light transport coefficients between the camera projector pair, thus overcoming the influence of interreflections. However, PSI requires a large number of measurements, causing poor capturing efficiency. To achieve efficient 3D shape measurement in the presence of strong interreflections, we present a deep-learning-based parallel single-pixel imaging method (dlPSI), featuring adopting a deep-learning network to achieve accurate light transport coefficient reconstruction and using sampling Fourier strategy to reduce measurements. The deep-learning network is designed to suppress the noise caused by undersampling the Fourier domains. Experiments prove that our dlPSI with the characteristic-based deterministic Fourier sampling strategy achieves high-quality 3D shape measurement while saving 94% of measurements.