Hyperspectral Image Denoising
17 papers with code • 3 benchmarks • 1 datasets
These leaderboards are used to track progress in Hyperspectral Image Denoising
Most implemented papers
Spatial-Spectral Transformer for Hyperspectral Image Denoising
Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications.
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network
Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the performance of the subsequent HSI interpretation and applications.
Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising
This is done by first learning a low-dimensional projection and the related reduced image from the noisy HSI.
3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising
In this paper, we propose an alternating directional 3D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge -- structural spatio-spectral correlation and global correlation along spectrum.
Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations
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.
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration
Hyperspectral imaging offers new perspectives for diverse applications, ranging from the monitoring of the environment using airborne or satellite remote sensing, precision farming, food safety, planetary exploration, or astrophysics.
Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation
Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion.
Hyperspectral Image Denoising Using SURE-Based Unsupervised Convolutional Neural Networks
Since SURE is an unbiased estimate of the mean squared error (MSE) of an estimator, training a CNN using the SURE loss can yield similar results as using the MSE with ground truth in supervised learning.
Hyperspectral Image Denoising With Realistic Data
On the other hand, we propose an accurate HSI noise model which matches the distribution of real data well and can be employed to synthesize realistic dataset.
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse Representations
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.