Abstract: Low-quality image enhancement is an important part of the perception system of unmanned wide-body vehicle in open-pit mine. The image acquired by monocular camera is susceptible to many factors such as dust,rain,snow and fog in mining area and violent vibration. Aiming at the problems of high noise and color distortion in the traditional image enhancement algorithm,an improved Retinex-Net algorithm is proposed to enhance the image of open-pit mine. Cyclic adversarial generation network and two-channel residual network are used to improve the enhancement and denoising parts. The cyclic adversarial generation network generates more natural and realistic enhanced results by learning the mapping relationship between low-light and normal-light images. The dual-channel residual network can effectively remove noise and artifacts in low-light images by processing brightness and chrominance information simultaneously. The experimental results show that the proposed method is superior to the existing methods in both objective and subjective evaluation indexes. The proposed Retinex-Net algorithm provides an effective scheme to solve the image quality problem in open-pit mine.