Data-Driven Tight Frame for Multi-channel Images and Its Application to Joint Color-Depth Image Reconstruction  

作　　者：Jin Wang Jian-Feng Cai 

机构地区：[1]Beijing Key Laboratory of Multimedia and Intelligent Software Technology,College of Metropolitan Transportation,Beijing University of Technology,Beijing 100124,China [2]Department of Mathematics,University of Iowa,Iowa City,IA 52242,USA

出　　处：《Journal of the Operations Research Society of China》2015年第2期99-115,共17页中国运筹学会会刊（英文）

基　　金：Jian-Feng Cai is partially supported by the National Natural Science Foundation of USA(No.DMS 1418737).

摘　　要：In image restoration,we usually assume that the underlying image has a good sparse approximation under a certain system.Wavelet tight frame system has been proven to be such an efficient system to sparsely approximate piecewise smooth images.Thus,it has been widely used in many practical image restoration problems.However,images from different scenarios are so diverse that no static wavelet tight frame system can sparsely approximate all of themwell.To overcome this,recently,Cai et.al.(Appl Comput Harmon Anal 37:89–105,2014)proposed a method that derives a data-driven tight frame adapted to the specific input image,leading to a better sparse approximation.The data-driven tight frame has been applied successfully to image denoising and CT image reconstruction.In this paper,we extend this data-driven tight frame construction method to multi-channel images.We construct a discrete tight frame system for each channel and assume their sparse coefficients have a joint sparsity.The multi-channel data-driven tight frame construction scheme is applied to joint color and depth image reconstruction.Experimental results show that the proposed approach has a better performance than state-of-the-art joint color and depth image reconstruction approaches.

关 键 词：Data-driven tight frame Group sparsity Image reconstruction 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]