Optimized sinusoidal patterns for high-performance computational ghost imaging

Abstract

Computational ghost imaging (CGI) can reconstruct scene images by two-order correlation between sampling patterns and detected intensities from a bucket detector. By increasing the sampling rates (SRs), imaging quality of CGI can be improved, but it will result in an increasing imaging time. Herein, in order to achieve high-quality CGI under an insufficient SR, we propose two types of novel sampling methods for CGI, to the best of our knowledge, cyclic sinusoidal-pattern-based CGI (CSP-CGI) and half-cyclic sinusoidal-pattern-based CGI (HCSP-CGI), in which CSP-CGI is realized by optimizing the ordered sinusoidal patterns through “cyclic sampling patterns,” and HCSP-CGI just uses half of the sinusoidal pattern types of CSP-CGI. Target information mainly exists in the low-frequency region, and high-quality target scenes can be recovered even at an extreme SR of 5%. The proposed methods can significantly reduce the sampling number and real-time ghost imaging possible. The experiments demonstrate the superiority of our method over state-of-the-art methods both qualitatively and quantitatively.