Fast high quality computational ghost imaging based on saliency variable sampling detection

Abstract Fast computational ghost imaging with high quality and ultra-high-definition resolution reconstructed images has important application potential in target tracking, biological imaging and other fields. However, as far as we know, the resolution (pixels) of the reconstructed image is related to the number of measurements. And the limited resolution of reconstructed images at low measurement times hinders the application of computational ghost imaging. Therefore, in this work, a new computational ghost imaging method based on saliency variable sampling detection is proposed to achieve high-quality imaging at low measurement times. This method physically variable samples the salient features and realizes compressed detection of computational ghost imaging based on the salient periodic features of the bucket detection signal. Numerical simulation and experimental results show that the reconstructed image quality of our method is similar to the compressed sensing method at low measurement times. Even at 500 (sampling rate $$0.76\%$$ 0.76 % ) measurement times, the reconstructed image of the method still has the target features. Moreover, the $$2160\times 4096$$ 2160 × 4096 (4K) pixels ultra-high-definition resolution reconstructed images can be obtained at only a sampling rate of $$0.11\%$$ 0.11 % . This method has great potential value in real-time detection and tracking, biological imaging and other fields.