no code implementations • 18 Sep 2021 • Lina Zhuang, Michael K. Ng
This paper introduces a fast and parameter-free hyperspectral image mixed noise removal method (termed FastHyMix), which characterizes the complex distribution of mixed noise by using a Gaussian mixture model and exploits two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain.
no code implementations • 30 Mar 2021 • Lianru Gao, Zhicheng Wang, Lina Zhuang, Haoyang Yu, Bing Zhang, Jocelyn Chanussot
Tensor-based methods have been widely studied to attack inverse problems in hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally represented as a third-order tensor, which can perfectly retain the spatial information in the image.
1 code implementation • 12 Mar 2021 • Lina Zhuang, Lianru Gao, Bing Zhang, Xiyou Fu, Jose M. Bioucas-Dias
Hyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels.
2 code implementations • 11 Mar 2021 • Lina Zhuang, Jose M. Bioucas-Dias
This paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian noise, and fast hyperspectral inpainting (FastHyIn), an inpainting algorithm to restore HSIs where some observations from known pixels in some known bands are missing.