Affected by insufficient nighttime illumination， some content in night vision imaging is prone to missing or blurring， resulting in poor colorization. To address this issue， this paper proposes a colorization algorithm of night vision images based on generative adversarial network， where the image colorization in the blurred area is improved through texture detail prediction. Firstly， in the blurred area restoration， down-sampling is used to gradually reduce the proportion of the blurred image patches. What’s more， gradient adjustment predictor is used to predict the pixel values around the blurred image patches so as to continuously enhance and remedy the blurred texture details. Then， in the colorization process， we use the super-resolution imaging and the advanced adversarial network colouring model to obtain a clearer color image through minimizing the brightness and texture distortions. Experimental results show that， the PSNR of gray image increases by 0.33 dB on average after the distortion and enhancement in the blurred area. Compared with the previous advanced colorization methods， the proposed method can give the grayscale night vision image richer and more natural colors， and express the details of the image more clearly. It helps to improve the efficiency of target detection and recognition.