Real-time physical compression computational ghost imaging based on array spatial light field modulation and deep learning

Abstract

Ghost imaging has great application potential in remote sensing observation and biomedical due to the use of a single pixel detector for imaging, which makes it easy to imaging in low light conditions and imperfect spectral regions of the camera. However, the higher the reconstructed image resolution (the more pixels), the larger the number of measurements are required, which makes it difficult to realize the real-time imaging required in the application. Therefore, a new real-time computational ghost imaging method based on deep learning technology and array spatial light field modulation is proposed, which can realize high-quality imaging with high magnification down sampling. Furthermore, the physical compression imaging of objects is realized by adjusting the array light field composed of several identical sub-light fields, and the original image of the object is successfully obtained from the compressed image by deep learning. The resulting compressed image is realized by the physical means of array light field regulation, and allows the far-field transmission of physical compressed image (or use the image compression algorithm for deep compression of the physical compressed image) with low storage data to reduce the transmission data, and then use deep learning to obtain the original image. We implement the proposed method in numerical simulation and real-time imaging experiments of multifold down sampling and physical compressed (eg, 4x, 9x, 16x), and demonstrate its validity and extensibility. This makes the method have a great potential application and prospect in the application scenarios of earth observation imaging and Limited space-time transmission window.