@inproceedings{e134d04fd45f4afa9aaecd47486ce6fe,
title = "L0 Gradient Regularized Low-Rank Tensor Model for Hyperspectral Image Denoising",
abstract = "Spatial spectral total variation has been widely used in the hyperspectral image (HSI) denoising algorithms. However, the total variation norm only penalizes the gradient magnitudes of HSIs, which may influence the recovery of denoised image edges. In this paper, we proposed a new l0 gradient regularized low-rank tensor model for removing HSI mixed noises. The low-rank tensor Tucker decomposition is applied for recovering the clean HSI part by using the HSI global correlation. The l0 gradient regularization controls the number of non-zero gradients directly and is designed to explore the spatial-spectral property of hyperspectral images and preserve important image features. The optimization problem is solved by the Augmented Lagrange Multiplier (ALM) method efficiently. The real-world data experiment demonstrates the better performance of our method.",
keywords = "ALM, HSI denoising, l0 gradient, low-rank tensor model, spatial-spectral property",
author = "Minghua Wang and Qiang Wang and Jocelyn Chanussot",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019 ; Conference date: 24-09-2019 Through 26-09-2019",
year = "2019",
month = sep,
doi = "10.1109/WHISPERS.2019.8920965",
language = "英语",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE Computer Society",
booktitle = "2019 10th Workshop on Hyperspectral Imaging and Signal Processing",
address = "美国",
}