Realtime Scene Enhancement of Low Light Image/Video

The quality of image and videos plays a vital role in case of real-time systems. Images are captured without sufficient illumination, lead to low dynamic range and high propensity for generating high noise levels. The processing ideas and networks of several mainstream low-light image enhancement neural network algorithms such as DRBN, DSLR, ZeroDCE, EnlightenGAN, as well as the key technologies used are compared and analyzed. In this paper, the EnglightenGAN neural network is used as the core image enhancement algorithm, and NVIDIA AGX Xavier equipment and video stream processing technology are used to achieve the entire process of video acquisition, video frame encoding, image enhancement processing, frame decoding, and video stream display. It can adapt to most low-light and dark scenes which has a good enhancement effect on video streams collected in low-light scenes. The real-time performance and image enhancement effect of the system achieved good results, with FPS reaching 10, SSIM reaching 0.51753, and PSNR reaching 27.89973.