Optimal parameters for image reconstruction in ghost imaging via sparsity constraints

Abstract

Ghost imaging via sparsity constraints (GISC) is an advanced imaging technique. The reconstruction quality of GISC is affected by the sparse ratio of the object, the regularization parameter, and the iteration number. Influences of these parameters on the peak signal-to-noise ratio (PSNR) of the reconstructed image are discussed and evaluated. The optimal regularization parameter and iteration number at different sparse ratios are given. Then the reconstructed images of GISC using the optimal parameters at different sparse ratios are shown. The improvement of the reconstruction quality of GISC utilizing the optimal parameters is confirmed through comparison with normalized ghost imaging. Finally, the reconstruction quality of GISC with random noise is analyzed, and a method to obtain the sparse ratio of the object by analyzing the signal of the bucket detector is discussed.