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The solar cell is key photovoltaic (PV) device that converts light energy into electricity, and its surface defects are the main reason for the decrease in the efficiency of PV systems. Conventional solar cell defect detection methods are often interfered with by cell substrates and are difficult to detect under high reflection, especially the overlap of substrates and defects. A solar cell defect detection method based on Fourier single-pixel imaging (FSI) is proposed to distinguish periodic substrates and defects in reconstruction by projecting illumination patterns with Fourier fringes, in which the removal of substrates reaches 100%. Meanwhile, for the high reflection present on the solar cell surface, the solar cell is directly treated as a single-pixel detector, which is free from interference from ambient light. The experiment adopts the solar cell itself as an imaging device under highly reflective conditions and removes the interference of the cell substrate by a 4-step phase-shifted FSI reconstruction algorithm to achieve the extraction of four types of defects, each of which is located in a different position of the solar cell substrate, at 10%, 50%, and 100% sampling rates. The method described here complements existing solar cell defect detection approaches and may be applied to enhance photovoltaic testers that rely on real-time correlation measurements, with promising applications in chip inspection and medical cell imaging. • Fourier single-pixel imaging(FSI) method for solar cells defect detection. • By using the solar cell as a photodetector(PD) to avoid the interference of high reflections. • Periodic substrate removal by 4-step phase-shift-based FSI reaches 100% in four different types of defects. [ABSTRACT FROM AUTHOR]

Degradation of imaging quality due to noise sources is one of the most essential problems for single-pixel imaging (SPI). This paper presents a method that separates, identifies, and reduces noise from the 1-D signal of the SPI before reconstructing the image. The proposed method raises the quality of the picture significantly, for example, PSNR from 9.14 dB to 30.29 dB and SSIM from 5.55% to 91.99% when SNR is -15 dB in simulation. This method has a strong robustness to a variety of noise types compared to the traditional filtering methods. The experimental results verified the proposed method's feasibility and improved the image's quality from a chaotic state to an acceptable stage. The method proposed in this paper can separate, identify and remove the noise component from the sources in SPI, which may open a branch by removing noise from sources rather than post-processing. • A method to separate, identify, and reduce noise in single-pixel imaging. • Robust to a variety of noise types compared to the traditional filtering methods. • Enhance the imaging quality and expand application of SPI in noisy conditions. [ABSTRACT FROM AUTHOR]

• A deep-hole imaging method by using MMF is proposed. • Wavelet polarization fusion is applied to deep-hole image reconstruction. • The method achieves reconstruction with a maximum spatial resolution of 3.18 lp/mm. Conventional deep hole imaging uses a camera array as the imaging device, with the lens entering the deep hole to obtain spatial light information for reconstruction. The lens often determines the size of the imaging aperture and it is hard to adapt to different sizes of deep holes, especially elongated ones. This paper proposes a method of the multimode fiber(MMF) deep hole imaging based on wavelet polarization fusion, completed with fiber single-pixel imaging(FSPI) and fiber polarization single-pixel imaging(FPSPI). The system completes three different sizes of deep holes imaging, respectively 8mm × 8mm, 5mm × 5mm, 3mm × 3mm. In addition, the information entropy of each 32 pixels image increases 12.26 % and 11.78 % relative to the original image, which is higher than that of 64 pixels images of the same class, with the spatial resolution of 3.18 lp/mm. The proposed deep hole imaging method based MMF is expected to be applied in pipeline sidewall imaging and deep hole 3D imaging. [ABSTRACT FROM AUTHOR]