Photon-limited face image super-resolution based on deep learning

With one single photon camera (SPC), imaging under ultra weak-lighting conditions may have wide-ranging applications ranging from remote sensing to night vision, but it may seriously suffer from the problem of under-sampled inherent in SPC detection. Some approaches have been proposed to solve the under-sampled problem by detecting the objects many times to generate high-resolution images and performing noise reduction to suppress the Poission noise inherent in low-flux operation. To address the under-sampled problem more effectively, a new approach is developed in this paper to reconstruct high-resolution images with lower-noise by seamlessly integrating low-light-level imaging with deep learning. In our new approach, all the objects are detected only once by SPC, where a deep network is learned to reduce noise and reconstruct high-resolution images from the detected noisy under-sampled images. In order to demonstrate our proposal is feasible, we first select a special category to verify by experiment, which are human faces. Such deep network is able to recover high-resolution and lower-noise face images from new noisy under-sampled face images and the resolution can achieve 4× up-scaling factor. Our experimental results have demonstrated that our proposed method can generate high-quality images from only ~0.2 detected signal photon per pixel.