Robust Low Transformed Multi-Rank Tensor Completion With Deep Prior Regularization for Multi-Dimensional Image Recovery

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yao Li;Duo Qiu;Xiongjun Zhang
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引用次数: 1

Abstract

In this article, we study the robust tensor completion problem in three-dimensional image data, where only partial entries are available and the observed tensor is corrupted by Gaussian noise and sparse noise simultaneously. Compared with the existing tensor nuclear norm minimization for the low-rank component, we propose to use the transformed tensor nuclear norm to explore the global low-rankness of the underlying tensor. Moreover, the plug-and-play (PnP) deep prior denoiser is incorporated to preserve the local details of multi-dimensional images. Besides, the tensor $\ell _{1}$ norm is utilized to characterize the sparseness of the sparse noise. A symmetric Gauss-Seidel based alternating direction method of multipliers is designed to solve the resulting model under the PnP framework with deep prior denoiser. Extensive numerical experiments on hyperspectral and multispectral images, videos, color images, and magnetic resonance image datasets are conducted to demonstrate the superior performance of the proposed model in comparison with several state-of-the-art models.
用于多维图像恢复的具有深度先验正则化的鲁棒低变换多阶张量补全
在本文中,我们研究了三维图像数据中的鲁棒张量补全问题,其中观测到的张量同时被高斯噪声和稀疏噪声破坏。与现有的针对低秩分量的张量核范数最小化方法相比,我们提出使用变换后的张量核范数来探索底层张量的全局低秩性。此外,采用即插即用(PnP)深度先验去噪来保留多维图像的局部细节。此外,利用张量$\ well _{1}$ _1范数表征稀疏噪声的稀疏性。在带深度先验去噪的PnP框架下,设计了一种基于高斯-赛德尔的对称交替方向乘子方法来求解得到的模型。在高光谱和多光谱图像、视频、彩色图像和磁共振图像数据集上进行了大量的数值实验,以证明与几种最先进的模型相比,所提出的模型具有优越的性能。
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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