Computational ghost imaging with 4-step iterative rank minimization

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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The proposal of a rank-minimization-based computational ghost imaging scheme results in the acquisition of clear ghost images in sub-Nyquist sampling ratio conditions. The scheme outperforms other methods in both numerical and practical experiments, demonstrating significant improvement in image quality and potential applications in various fields.