融合深度图像先验的全变差图像着色算法  

Total variation algorithm with depth image priors for image colorization

作　　者：张玺 金正猛[1] 姜亚琴[1] ZHANG Xi;JIN Zhengmeng;JIANG Yaqin(Shool of Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

机构地区：[1]南京邮电大学理学院,江苏南京210023

出　　处：《系统工程与电子技术》2022年第2期385-393,共9页Systems Engineering and Electronics

基　　金：国家自然科学基金(11771005)资助课题。

摘　　要：提出了融合深度图像先验的全变差(total variation,TV)图像着色模型,在即插即用(plug-and-play,PnP)框架下,结合交替方向乘子法(alternating direction method of multipliers,ADMM),设计出相应的数值求解算法,并给出该算法的收敛性结果。数值实验结果表明,该模型能有效整合耦合TV边缘捕获和卷积神经网络(convolutional neural network,CNN)细节捕捉的功能,对结构图像和纹理等细节丰富的图像,均能实现较大范围的有效着色。In this paper,we propose a total variation(TV)model with depth image priors for image colorization.Under the plug-and-play(PnP)framework,we design the numerical algorithm to solve the model by incorporating the alternating direction method of multipliers(ADMM),and give the convergence result of the algorithm.The experimental results show that the model can effectively integrate the edge capture function of coupled TV and the detail capture function of convolutional neural network(CNN),and also can achieve a large scale effective coloring for structural images and multi textures detailed images.

关 键 词：图像着色 耦合全变差 卷积神经网络 即插即用框架 交替方向乘子算法 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]