Efficient Fourier single-pixel imaging based on weighted sorting

Abstract

Fourier single-pixel imaging (FSI) has attracted increased attention in recent years with the advantages of a wide spectrum range and low cost. FSI reconstructs a scene by directly measuring the Fourier coefficients with a single-pixel detector. However, the existing sampling method is difficult to balance the noise suppression and image details within a limited number of measurements. Here we propose a new sampling strategy for FSI to solve this problem. Both the generality of the spectral distribution of natural images in the Fourier domain and the uniqueness of the spectral distribution of the target images in the Fourier domain are considered in the proposed method. These two distributions are summed with certain weights to determine the importance of the Fourier coefficients. Then these coefficients are sampled in order of decreasing importance. Both the simulations and experiments demonstrate that the proposed method can capture more key Fourier coefficients and retain more details with lower noise. The proposed method provides an efficient way for Fourier coefficient acquisition.