The combination of a single-photon avalanche diode detector with a high-sensitivity and photon-efficient reconstruction algorithm can realize the reconstruction of target range image from weak light signal conditions. The limited spatial resolution of the detector and the substantial background noise remain significant challenges in the actual detection process, hindering the accuracy of 3D reconstruction techniques. To address this challenge, this paper proposes a denoising super-resolution reconstruction network based on generative adversarial network (GAN) design. Soft thresholding is incorporated into the deep architecture as a nonlinear transformation layer to effectively filter out noise. Moreover, the Unet-based discriminator is introduced to complete the high-precision detail reconstruction. The experimental results show that the proposed network can achieve high-quality super-resolution range imaging. This approach has the potential to enhance the accuracy and quality of long-range imaging in weak light signal conditions, with broad applications in fields such as robotics, autonomous vehicles, and biomedical imaging.