High-performance multispectral ghost imaging based on the sine–cosine optimized patterns

In recent years, the recovery of multispectral target scene has garnered increasing attentions from researchers, leading to the development of a series of ghost imaging schemes. However, the existing schemes still possess limitations such as requiring a large number of measurements and subpar performance. Therefore, here, we propose a deep-learning driven multispectral ghost imaging (MGI) scheme based on the sine–cosine optimized patterns (SCOP) for high-efficiency MGI. This scheme adopts a modified pattern selection strategy and relies on the powerful feature-extraction and representation-learning capabilities of multi-scale colour mapping (MSCM) network, which promise high-efficiency MGI for the multispectral target scenes. Experimental results show that the proposed MGI scheme can reconstruct complex multispectral target scenes with high quality at an ultra-low sampling rate (SR) of 2 %. In addition, the proposed scheme has excellent anti-noise performance and performs well in low signal-to-noise ratio (SNR) of 10 dB conditions. Overall, it provides a reliable solution for achieving fast high-quality MGI.