Abstract
We propose a rank-minimization-based computational ghost imaging (CGI) scheme to acquire clear ghost images in sub-Nyquist sampling ratio (SR) conditions. The proposed scheme uses a 4-step iterative method that is composed of block matching, weighted nuclear norm minimization, aggregation and projection for the CGI image reconstruction. Both numerical and practical experiments are implemented, and the results are compared with those of four recently published works, “Russian dolls” CGI, 4-connected-region-based CGI, “Cake-Cutting” CGI, and compressive-sensing-based CGI. The comparison results demonstrate that the image quality of the proposed scheme is dramatically enhanced and outperforms the other four methods. The proposed scheme can be used in many practical application areas, such as remote sensing, underwater and X-ray CGI.
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