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

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 % ) measurement times, the reconstructed image of the method still has the target features. Moreover, the 2160 × 4096 (4K) pixels ultra-high-definition resolution reconstructed images can be obtained at only a sampling rate of 0.11 % . This method has great potential value in real-time detection and tracking, biological imaging and other fields.

Low sampling high quality image reconstruction and segmentation based on array network ghost imaging.

High-quality imaging under low sampling time is an important step in the practical application of computational ghost imaging (CGI). At present, the combination of CGI and deep learning has achieved ideal results. However, as far as we know, most researchers focus on one single pixel CGI based on deep learning, and the combination of array detection CGI and deep learning with higher imaging performance has not been mentioned. In this work, we propose a novel multi-task CGI detection method based on deep learning and array detector, which can directly extract target features from one-dimensional bucket detection signals at low sampling times, especially output high-quality reconstruction and image-free segmentation results at the same time. And this method can realize fast light field modulation of modulation devices such as digital micromirror device to improve the imaging efficiency by binarizing the trained floating-point spatial light field and fine-tuning the network. Meanwhile, the problem of partial information loss in the reconstructed image due to the detection unit gap in the array detector has also been solved. Simulation and experimental results show that our method can simultaneously obtain high-quality reconstructed and segmented images at sampling rate of 0.78 %. Even when the signal-to-noise ratio of the bucket signal is 15 dB, the details of the output image are still clear. This method helps to improve the applicability of CGI and can be applied to resource-constrained multi-task detection scenarios such as real-time detection, semantic segmentation, and object recognition.