Self-adaptive sampling for Fourier single-pixel imaging using probability estimation

Fourier single-pixel imaging (FSI) produces images through the acquisition of Fourier domain information using a single-pixel detector and a sequence of light modulation patterns. Conventional sampling approach in FSI tends to perform poorly when it comes to capturing the intricate details present in high-frequency components of the image. The variable density sampling method follows a predefined mechanism where the power of image information decreases when frequency increases. To enhance the sampling efficiency, we propose a self-adaptive sampling method to dynamically determine the order of the illumination pattern based on the scene's spectrum distribution through the probability estimation of the low-frequency samples. The image is subsequently reconstructed through compressed sensing technique. Our results indicate improved image quality even at low sampling ratios. Unlike existing adaptive approaches, the proposed method does not require dataset training or redundant sampling. Instead, it exhibits remarkable versatility by adapting to various types of images.