Results 21 to 30 of about 1,639 (207)

Memory Augmentation and Non-Local Spectral Attention for Hyperspectral Denoising

Remote Sensing
In this paper, a novel hyperspectral denoising method is proposed, aiming at restoring clean images from images disturbed by complex noise. Previous denoising methods have mostly focused on exploring the spatial and spectral correlations of hyperspectral
Le Dong, Yige Mo, Hao Sun, Fangfang Wu, Weisheng Dong   +4 more
doaj   +2 more sources

Multiscale Adaptive Fusion Network for Hyperspectral Image Denoising

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Removing the noise and improving the visual quality of hyperspectral images (HSIs) is challenging in academia and industry. Great efforts have been made to leverage local, global, or spectral context information for HSI denoising.
Haodong Pan, Feng Gao, Junyu Dong, Qian Du   +3 more
doaj   +3 more sources

Hyperspectral Image Denoising via Low-Rank Tucker Decomposition with Subspace Implicit Neural Representation

Remote Sensing
Hyperspectral image (HSI) denoising is an important preprocessing step for downstream applications. Fully characterizing the spatial-spectral priors of HSI is crucial for denoising tasks.
Cheng Cheng, Dezhi Sun, Yaoyuan Yang, Zhoucheng Guo, Jiangjun Peng   +4 more
doaj   +2 more sources

Hyperspectral Image Denoising Based on Non-local Similarity and Weighted-truncated NuclearNorm [PDF]

Jisuanji kexue, 2021
Due to the interference of instrumental noise,hyperspectral images (HSI) are often corrupted to some extent by Gaussian noise,which will seriously affect the subsequent performance of image processing.Therefore,image denoising has been considered as an ...
ZHENG Jian-wei, HUANG Juan-juan, QIN Meng-jie, XU Hong-hui, LIU Zhi
doaj   +1 more source

Deep spectral unmixing framework via 3D denoising convolutional autoencoder

IET Image Processing, 2021
Hyperspectral unmixing is an important technique which attempts to acquire pure spectra of distinct substances (endmembers) and estimate fractional abundances from highly mixed pixels. This paper proposed a novel deep network‐based framework for unmixing
Peiyuan Jia, Miao Zhang, Yi Shen
doaj   +1 more source

Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing

Applied Sciences, 2022
Hyperspectral data are important for water color remote sensing. The inevitable noise will devalue its application. In this study, we developed a 1-D denoising method for water hyperspectral data, based on sparse representing.
Yulong Guo, Qingsheng Bi, Yuan Li, Chenggong Du, Junchang Huang, Weiqiang Chen, Lingfei Shi, Guangxing Ji   +7 more
doaj   +1 more source

NL3DLogTNN: An Effective Hyperspectral Image Denoising Method Combined Non-Local Self-Similarity and Low-Fibered- Rank Regularization

IEEE Access, 2023
Hyperspectral image denoising is an important research topic in the field of remote sensing image processing. Recently, methods based on non-local low-rank tensor approximation have gained widespread attention towing to their ability to fully exploit non-
Haoran Liu, Tianyu Su, Xinzhe Du, Yuxin Zhai, Jianli Zhao   +4 more
doaj   +1 more source

Hyperspectral Image Denoising with Composite Regularization Models [PDF]

Journal of Sensors, 2016
Denoising is a fundamental task in hyperspectral image (HSI) processing that can improve the performance of classification, unmixing, and other subsequent applications. In an HSI, there is a large amount of local and global redundancy in its spatial domain that can be used to preserve the details and texture.
Ao Li 0002, Deyun Chen, Kezheng Lin, Guanglu Sun   +3 more
openaire   +1 more source

Hyperspectral Image Denoising With Log-Based Robust PCA [PDF]

2021 IEEE International Conference on Image Processing (ICIP), 2021
It is a challenging task to remove heavy and mixed types of noise from Hyperspectral images (HSIs). In this paper, we propose a novel nonconvex approach to RPCA for HSI denoising, which adopts the log-determinant rank approximation and a novel $\ell_{2,\log}$ norm, to restrict the low-rank or column-wise sparse properties for the component matrices ...
Yang Liu, Qian Zhang, Yongyong Chen, Qiang Cheng 0001, Chong Peng 0001   +4 more
openaire   +2 more sources

Hyperspectral Image Denoising via Low-Rank Representation and CNN Denoiser [PDF]

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Hyperspectral images (HSIs) are widely used in various tasks such as earth observation and target detection. However, during the imaging process, HSIs are often corrupted by various noises. In this article, we firstly investigate the advantages of traditional physical restoration models and the denoising convolutional neural networks (CNN) for HSIs ...
Hezhi Sun, Ming Liu 0014, Ke Zheng, Dong Yang, Jindong Li 0003, Lianru Gao   +5 more
openaire   +2 more sources