Improving the Signal-to-Noise Ratio of Superresolution Imaging Based on Single-Pixel Camera

Abstract

Based on the theories of single-pixel camera and compressed sensing image reconstruction, the sparse basis, the projection method of measurement matrix, and the signal reconstruction algorithm are optimized. First, for the sparse representation of image, the restraint matrix is designed combining the characteristics of wavelet sparse coefficients to enhance the sparse representation ability of the discrete wavelet base, which improves the quality of image reconstruction and imaging by single-pixel camera. Then, in the single-pixel camera system, the projection method is improved according to the characteristics of the digital mirror device micromirror, and a new bilateral projection method based on the block diagonal measurement matrix is proposed to realize the superresolution imaging of the outdoor scene, reducing the number of measurement and improving the imaging quality. Finally, for the algorithm of image reconstruction, combining the characteristics of convex optimization and nonconvex optimization algorithms, a new algorithm from convex optimization approximately to nonconvex optimization algorithm is proposed, and compared with the traditional image reconstruction algorithm, such as greedy pursuit algorithm, minimum norm algorithm, and image interpolation, the peak signal-to-noise ratio and imaging quality of reconstructed images are effectively improved. The feasibility of the proposed methods is demonstrated by simulation experiments and imaging experiments of single-pixel camera.