湖南大学学报（自然科学版）2016，Vol.43Issue(10)：148-154,7.
基于张量秩校正的图像恢复方法∗
Tensor Rank Corrected Procedure for Image Restoration
摘要
Abstract
Tensor-based restoration of medical images and video images was studied with limited sam-ples.On the basis of the theory of tensor singular value decomposition (t-SVD),a tensor rank-correction model (CRTNN)was proposed to correct the tensor nuclear norm minimization model (TNN).A two-stage rank correction method is given as follows:the first stage is used to generate a pre-estimator by sol-ving the TNN model,and the second stage is to solve the CRTNN model to generate a high-accuracy re-covery by the pre-estimator.A tensor proximal point algorithm was proposed to solve the CRTNN model and the TNN model,making it possible to calculate tensor directly in the real field.The convergence of the algorithm was proved in theory.Numerical experiments of medical images and video images verify the effi-ciency of the proposed model and method.The experiment results show that tensor rank-correction model and method can achieve higher-accuracy recovery.关键词
图像恢复/张量奇异值分解/张量秩校正/张量近似点算法Key words
image restoration/t-SVD/tensor rank-correction model/tensor proximal point algorithm分类
信息技术与安全科学引用本文复制引用
白敏茹,黄孝龙,顾广泽,赵雪莹..基于张量秩校正的图像恢复方法∗[J].湖南大学学报（自然科学版）,2016,43(10):148-154,7.基金项目
国家自然科学基金资助项目(11571098),National Natural Science Foundation of China(11571098) （11571098）
湖南省高校创新平台开放基金资助项目(14K018) （14K018）
