Source separation and noise reduction in single-pixel imaging

Abstract

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.