Image Reconstruction for Quanta Image Sensors Using Deep Neural Networks

Abstract

Quanta Image Sensor (QIS) is a single-photon image sensor that oversamples the light field to generate binary measurements. Its single-photon sensitivity makes it an ideal candidate for the next generation image sensor after CMOS. However, image reconstruction of the sensor remains a challenging issue. Existing image reconstruction algorithms are largely based on optimization. In this paper, we present the first deep neural network approach for QIS image reconstruction. Our deep neural network takes the binary bit stream of QIS as input, learns the nonlinear transformation and denoising simultaneously. Experimental results show that the proposed network produces significantly better reconstruction results compared to existing methods.