As we know that dark images have mainly two aspects which make its study a difficult task. They are its low dynamic range and high propensity for generating high noise levels. Hence an approach based on deep learning based system is adopted. For this purpose we propose Generative Adversarial Network (GAN) based Extremely Dark Video Enhancement Network (GEVE) model. Main idea of GEVE is to team the model with low /normal- light image pairs. When this is done GAN network learn the translation from light feeble images and images captured under normal illumination and automatically translate original images taken under extremely low light conditions into images of quality we want. It is found that GEVE proposed by us outperforms the hither to known state-of-art techniques in enhancement. We are the view that the proposed system is an ideal candidate to handle dark image/video frames.

众所周知，暗图像主要有两个方面使其研究成为一项艰巨的任务。它们是低动态范围和产生高噪音水平的高倾向。因此采用了基于深度学习的系统的方法。为此，我们提出了基于生成对抗网络 (GAN) 的极暗视频增强网络 (GEVE) 模型。GEVE 的主要思想是将模型与低/正常光图像对组合在一起。完成此操作后，GAN 网络从微弱的图像和正常光照下捕获的图像中学习翻译，并自动将在极低光照条件下拍摄的原始图像转换为我们想要的质量图像。发现我们提出的 GEVE 在增强方面优于迄今为止已知的最先进技术。