AIDEDNet: Anti-interference and detail enhancement dehazing network for real-world scenes
J Zhang, F He, Y Duan, S Yang - Frontiers of Computer Science, 2023 - Springer
The haze phenomenon seriously interferes the image acquisition and reduces image
quality. Due to many uncertain factors, dehazing is typically a challenge in image …
quality. Due to many uncertain factors, dehazing is typically a challenge in image …
Multimodal core tensor factorization and its applications to low-rank tensor completion
Low-rank tensor completion has been widely used in computer vision and machine learning.
This paper develops a novel multimodal core tensor factorization (MCTF) method combined …
This paper develops a novel multimodal core tensor factorization (MCTF) method combined …
Hyperspectral image denoising with weighted nonlocal low-rank model and adaptive total variation regularization
Y Chen, W Cao, L Pang, X Cao - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Hyperspectral image (HSI) is always corrupted by various types of noises during image
capturing, such as Gaussian noise, stripe noise, deadline noise, impulse noise, and more …
capturing, such as Gaussian noise, stripe noise, deadline noise, impulse noise, and more …
Hyperspectral image denoising by asymmetric noise modeling
In general, hyperspectral images (HSIs) are degraded by a mixture of complicated noise (ie,
mixture of Gaussian and sparse noise), and how to precisely model HSI noise plays a vital …
mixture of Gaussian and sparse noise), and how to precisely model HSI noise plays a vital …
Tensor train factorization under noisy and incomplete data with automatic rank estimation
As a powerful tool in analyzing multi-dimensional data, tensor train (TT) decomposition
shows superior performance compared to other tensor decomposition formats. Existing TT …
shows superior performance compared to other tensor decomposition formats. Existing TT …
Hyperspectral image denoising via self-modulating convolutional neural networks
Compared to natural images, hyperspectral images (HSIs) consist of a large number of
bands, with each band capturing different spectral information from a certain wavelength …
bands, with each band capturing different spectral information from a certain wavelength …
Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising
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 …
agriculture and industry. Nevertheless acquiring high signal-to-noise ratio HSI poses a …
Enhanced total variation regularized representation model with endmember background dictionary for hyperspectral anomaly detection
C Zhao, C Li, S Feng, X Jia - IEEE Transactions on Geoscience …, 2021 - ieeexplore.ieee.org
In recent years, several representation models based on total variation (TV) have been
proposed for hyperspectral imagery (HSI) anomaly detection. However, the TV terms of …
proposed for hyperspectral imagery (HSI) anomaly detection. However, the TV terms of …
All of low-rank and sparse: A recast total variation approach to hyperspectral denoising
Hyperspectral image (HSI) processing tasks frequently rely on spatial–spectral total variation
(SSTV) to quantify the local smoothness of image structures. However, conventional SSTV …
(SSTV) to quantify the local smoothness of image structures. However, conventional SSTV …
Degradation-noise-aware deep unfolding transformer for hyperspectral image denoising
Hyperspectral imaging (HI) has emerged as a powerful tool in diverse fields such as medical
diagnosis, industrial inspection, and agriculture, owing to its ability to detect subtle …
diagnosis, industrial inspection, and agriculture, owing to its ability to detect subtle …