Hyperspectral Image Restoration Combining Intrinsic Image Characterization With Robust Noise Modeling

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 1628 - 1644
• Main Authors: Ma, Tian-Hui, Xu, Zongben, Meng, Deyu, Zhao, Xi-Le
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Convolutional neural network (CNN); Correlation; Corruption; Deep learning; Degradation; hyperspectral image (HSI) restoration; Hyperspectral imaging; Image restoration; low-rank tensor approximation; maximum a posteriori (MAP); Neural networks; Noise; noise modeling; Probabilistic logic; Probabilistic models; Restoration; Statistical analysis; Tensors
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2020.3046488

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.