High-quality computational ghost imaging using ADMM fused with low-rank regularization

Computational ghost imaging (CGI) has emerged as a promising technique for diverse imaging applications, particularly in challenging environments. However, achieving high-quality image reconstruction under low sampling rates and noisy conditions remains a significant challenge hindering practical deployment. To overcome these limitations and achieve superior reconstruction quality, we present a novel CGI method based on the Alternating direction method of multipliers (ADMM) fused with Low-rank regularization (GIAL). We also develop a fiber-based ghost imaging setup for experimental validation. Numerical simulations and experimental results validate the exceptional and general reconstruction performance of the proposed GIAL algorithm. Our findings demonstrate the algorithm's remarkable capacity to reconstruct high-quality images at extremely low sampling rates (e.g., 1.56%) and highlight its inherent robustness to noise. These superior characteristics underscore the significant potential of the GIAL method for widespread applications in biomedical imaging and remote sensing scenarios. (To foster transparency and reproducibility, the complete implementation of GIAL is available at https://gitee.com/dlammm2066/GIAL , subject to journal policy). • Proposed ADMM with low-rank regularization for ghost imaging at 1.56% sampling rate. • Validated universal applicability across 210+ diverse images from the USC-SIPI database. • Established theoretical framework for fiber-based ghost imaging with practical experimental validation.