Results 101 to 110 of about 1,639 (207)

Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Hyperspectral images (HSIs) have extensive applications in various fields such as medicine, agriculture, and industry. Nevertheless, acquiring high signal-to-noise ratio HSI poses a challenge due to narrow-band spectral filtering. Consequently, the importance of HSI denoising is substantial, especially for snapshot hyperspectral imaging technology ...
Zeng, Haijin, Cao, Jiezhang, Zhang, Kai, Chen, Yongyong, Luong, Hiep, Philips, Wilfried   +5 more
openaire   +2 more sources

SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising

Denoising is a crucial preprocessing step for hyperspectral images (HSIs) due to noise arising from intraimaging mechanisms and environmental factors. Long-range spatial-spectral correlation modeling is beneficial for HSI denoising but often comes with ...
Fu, G, Xiong, F, Zhou, J, Lu, J
core   +1 more source

Hybrid-Domain Synergistic Transformer for Hyperspectral Image Denoising

Applied Sciences
Hyperspectral image (HSI) denoising is challenged by complex spatial-spectral noise coupling. Existing deep learning methods, primarily designed for RGB images, fail to address HSI-specific noise distributions and spectral correlations.
Haoyue Li, Di Wu
doaj   +1 more source

Hyperspectral Image Denoising Based on Principal-Third-Order-Moment Analysis

Remote Sensing
Denoising serves as a critical preprocessing step for the subsequent analysis of the hyperspectral image (HSI). Due to their high computational efficiency, low-rank-based denoising methods that project the noisy HSI into a low-dimensional subspace ...
Shouzhi Li, Xiurui Geng, Liangliang Zhu, Luyan Ji, Yongchao Zhao   +4 more
doaj   +1 more source

Wavelet-Enhanced Weighted and Dense Cross-Scope Transformer for Hyperspectral Image Super-Resolution

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral image (HSI) super-resolution is crucial to enhance the spatial detail of hyperspectral data and has achieved notable advancements in recent years.
Kaiyu Zhang, Ping Xu, Jiayi Chen, Xiaoli Li   +3 more
doaj   +1 more source

Hyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations

, 2020
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.
Zhang, Bing, Gao, Lianru, Bioucas-Dias, Jose M., Zhuang, Lina, Fu, Xiyou   +4 more
core   +1 more source

Hyperspectral Image Mixed Denoising via Robust Representation Coefficient Image Guidance and Nonlocal Low-Rank Approximation

Recently, hyperspectral image (HSI) mixed denoising methods based on nonlocal subspace representation (NSR) have achieved significant success. However, most of these methods focus on optimizing the denoiser for representation coefficient images (RCIs ...
Baolong Guo, Chao Wang, Fangliang He, Cheng Li, Zhe Yuan, Jiawei Song   +5 more
core   +1 more source

Background Suppression by Multivariate Gaussian Denoising Diffusion Model for Hyperspectral Target Detection

Remote Sensing
Hyperspectral image (HSI) target detection plays a critical role in both military and civilian applications, including military reconnaissance, environmental monitoring, and precision agriculture.
Weile Han, Yuteng Huang, Jiaqi Feng, Rongting Zhang, Guangyun Zhang   +4 more
doaj   +1 more source

Preventing Overfitting in Deep Image Prior for Hyperspectral Image Denoising

CoRR
Deep image prior (DIP) is an unsupervised deep learning framework that has been successfully applied to a variety of inverse imaging problems. However, DIP-based methods are inherently prone to overfitting, which leads to performance degradation and necessitates early stopping.
Panagiotis Gkotsis, Athanasios A. Rontogiannis   +1 more
openaire   +2 more sources

Unsupervised Adaptation Learning for Real Multiplatform Hyperspectral Image Denoising

Real hyperspectral images (HSIs) are ineluctably contaminated by diverse types of noise, which severely limits the image usability. Recently, transfer learning has been introduced in hyperspectral denoising networks to improve model generalizability ...
Heiskanen, Janne, Luo, Zhaozhi, Zhong, Yanfei, Wang, Xinyu, Pellikka, Petri   +4 more
core   +1 more source