基于密集神经网络的灰度图像着色算法  

Grayscale image colorization algorithm based on dense neural network

作　　者：张娜[1] 秦品乐[1] 曾建潮[1] 李启 ZHANG Na;QIN Pinle;ZENG Jianchao;LI Qi(School of Data Science And Technology,North University of China,Taiyuan Shanxi 030051,China)

机构地区：[1]中北大学大数据学院

出　　处：《计算机应用》2019年第6期1816-1823,共8页journal of Computer Applications

摘　　要：针对在灰度图像着色领域中,传统算法信息提取率不高、着色效果不理想的问题,提出了基于密集神经网络的灰度图像着色算法,以实现改善着色效果,让人眼更好地观察图片信息的目的。利用密集神经网络的信息提取高效性,构建并训练了一个端到端的深度学习模型,对图像中的各类信息及特征进行提取。训练网络时与原图像进行对比,以逐渐减小网络输出结果的信息、分类等各类型的损失。训练完成后,只需向网络输入一张灰度图片,即可生成一张颜色饱满、鲜明逼真的彩色图片。实验结果表明,引入密集网络后,可有效改善着色过程中的漏色、细节信息损失、对比度低等问题,所提算法着色效果较基于VGG网络及U-Net、双流网络结构、残差网络(ResNet)等性能优异的先进着色算法而言取得了显著的改进。Aiming at the problem of low information extraction rate of traditional methods and the unideal coloring effect in the grayscale image colorization field, a grayscale image colorization algorithm based on dense neural network was proposed to improve the colorization effect and make the information of image be better observed by human eyes. With making full use of the high information extraction efficiency of dense neural network, an end-to-end deep learning model was built and trained to extract multiple types of information and features in the image. During the training, the loss of the network output result(such as information loss and classification loss) was gradually reduced by comparing with the original image. After the training, with only a grayscale image input into the trained network, a full and vibrant vivid color image was able to be obtained. The experimental results show that the introduction of dense network can effectively alleviate the problems such as color leakage, loss of detail information and low contrast, during the colorization process. The coloring effect has achieved significant improvement compared with the current advanced coloring methods based on Visual Geometry Group(VGG)-net, U-Net, dual stream network structure, Residual Network(ResNet), etc.

关 键 词：图像着色 密集神经网络 灰度图像 特征利用 信息损失 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]