Optical imaging is a crucial and direct technique to obtain information, and plays a pivotal role in science and technologies. However, in practical environments, the presence of fog can degrade quality of image, to even invisible. To reconstruct clear images in the presence of fog remains a daunting task in the field of optical imaging. Ghost imaging can suppress noise that is uncorrelated to illumination. This significantly enhances imaging robustness under noisy environment. In this paper, we built a time-gated computational ghost imaging system and evaluate the capability to reconstruct image under weak echoes. Furthermore, we use the imaging system to obtain clear pictures in foggy environments.
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.
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