基于生成对抗网络的渐进式夜视图像彩色化算法

Progressive Colorization Algorithm of Night Vision Images Based on Generative Adversarial Network

受限于夜景光照不足等影响,夜视成像中的部分内容极易缺失或模糊,导致这部分的彩色化效果不佳.为此,本文提出了一种基于生成对抗网络的夜视图像彩色化算法,通过对纹理细节的修复来提升图像模糊区域的彩色化效果.首先,在模糊区域修复中,利用下采样操作减少模糊图像块的比例,并用梯度调节预测器对模糊图像块周围的像素值进行预测,以此来不断增强和修复模糊的纹理细节.其次,在彩色化过程中,依托于生成的超分辨率图像和已有的先进对抗网络着色模型,通过最小化亮度和纹理等失真,来生成较为清晰的彩色图像.实验结果表明,经过模糊区域恢复和增强之后,灰度图像的PSNR平均提升0.33 dB.相比之前的夜视图像彩色化方法,本文方法可以赋予灰度夜视图像更丰富、自然的色调,更清楚地表达图像的细节,从而提高目标探测和识别效率.

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.