Hyperspectral Image Restoration Combining Intrinsic Image Characterization With Robust Noise Modeling

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Xi-Le Zhao¹  Tian-Hui Ma¹  Zongben Xu²  Deyu Meng³ 

[1] School of Mathematics and Statistics and the Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi&x2019;an Jiaotong University, Xi&x2019;an, China;

关键词: Convolutional neural network (CNN);    hyperspectral image (HSI) restoration;    low-rank tensor approximation;    maximum $a\;    posteriori$ (MAP);    noise modeling;   

DOI  :  10.1109/JSTARS.2020.3046488

来源: DOAJ

【 摘 要 】

In hyperspectral image (HSI) processing, a fundamental issue is to restore HSI data from various degradations such as noise corruption and information missing. However, most existing methods more or less ignore the abundant prior knowledge on HSIs and the embedded noise, leading to suboptimal performance in practice. In this article, we propose a novel HSI restoration method by fully considering the intrinsic image structures and the complex noise characteristics. For HSIs, the global correlation is captured by the Kronecker-basis-representation-based tensor low-rankness measure, which integrates the insights delivered by both CP and Tucker decompositions; the local regularity is depicted by a plug-and-play spatial-spectral convolutional neural network with strong fitting ability to complex image features. For realistic noise, its statistical characteristics are encoded by a nonidentical and nonindependent distributed mixture of Gaussians distribution with flexible fitting capability. Then, we incorporate these image and noise priors into a probabilistic model based on the maximum a posteriori principle, and develop a solving scheme by combining expectation-maximization and alternating direction method of multipliers. Extensive experimental results on both simulated and real scenarios demonstrate the effectiveness of the proposed method and its superiority over the compared state-of-the- arts.

【 授权许可】

Unknown