Online Denoising Single-Pixel Imaging Using Filtered Patterns

Abstract

Noise is inevitable in single-pixel imaging (SPI). Although post-processing algorithms can significantly improve image quality, they introduce additional processing time. To address this issue, we propose an online denoising single-pixel imaging scheme at the sampling stage, which uses the filter to optimize the illumination modulation patterns. The image is retrieved through the second-order correlation between the modulation patterns and the intensities detected by the single-pixel detector. Through simulations and experiments, we analyzed the impact of sampling rate, noise intensity, and filter template on the reconstructed images of both binary and grayscale objects. The results demonstrate that the denoising effect is comparable to the imaging-first followed by post-filtering procedures, but the post-processing time is reduced for the same image quality. This method offers a new way for rapid denoising in SPI, and it should be particularly advantageous in applications where time-saving is of paramount importance, such as in image-free large target classification.