Results 31 to 40 of about 539 (168)
SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising
, 2022 Deep learning (DL) based hyperspectral images (HSIs) denoising approaches directly learn the nonlinear mapping between noisy and clean HSI pairs. They usually do not consider the physical characteristics of HSIs.
Jiantao Zhou +11 more
core +1 more source
Non-local similarity based tensor decomposition for hyperspectral image denoising [PDF]
, 2017 Compared to traditional color or grayscale images, hyperspectral image (HSI) can help deliver more faithful representation of ground objects and enhance the performance of many computer vision tasks.
Jun Zhou +5 more
core +1 more source
MAC-Net: Model Aided Nonlocal Neural Network for Hyperspectral Image Denoising
, 2021 Hyperspectral image (HSI) denoising is an ill-posed inverse problem. The underlying physical model is always important to tackle this problem, which is unfortunately ignored by most of the current deep learning (DL)-based methods, producing poor ...
Zhao, Q +4 more
core +1 more source
Learning a Model-Based Deep Hyperspectral Denoiser from a Single Noisy Hyperspectral Image
, 2021 Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the quality of HSI. Model-based methods take the degradation model and the structure of underlying clean HSI into account for denoising but require a large number of ...
Shuyin Tao +11 more
core +1 more source
EURASIP Journal on Advances in Signal Processing, 2022 During the acquisition of a hyperspectral image (HSI), it is easily corrupted by many kinds of noises, which limits the subsequent applications. For decades, numerous HSI denoising methods have been proposed.
Zhi Zhang, Fang Yang
doaj +1 more source
Reconstruction of Compressed Hyperspectral Image Using SqueezeNet Coupled Dense Attentional Net
Remote Sensing, 2023 This study addresses image denoising alongside the compression and reconstruction of hyperspectral images (HSIs) using deep learning techniques, since the research community is striving to produce effective results to utilize hyperspectral data.
Divya Mohan +2 more
doaj +1 more source
Spatial-Spectral Convolutional Sparse Neural Network for Hyperspectral Image Denoising
, 2022 Sparse representation (SR) is a widely accepted hyper-spectral image (HSI) denoising model. Because of the curse of dimensionality and the desire to better fit the data, the SR models are typically deployed on small and fully overlapping blocks whose ...
Xiong, F, Zhou, J, Ye, M, Qian, Y
core +1 more source
Broadband CARS Hyperspectral Classification of Single Immune Cells. [PDF]
J BiophotonicsWe report on a novel approach to single immune cell classification using Broadband CARS hyperspectral imaging. This work implements a raster scanning approach and a custom semiautomated cell segmentation and preprocessing pipeline. Known cell types were used to train a classifier and then we use the trained model on a mixture of unlabeled cells on the ...
Muddiman R +3 more
europepmc +2 more sources
Spatial-Spectral Transformer for Hyperspectral Image Denoising
, 2022 Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications. Unfortunately, though witnessing the development of deep learning in HSI denoising area, existing convolution-based methods face the trade-off ...
Fu, Ying, Zhang, Yulun, Li, Miaoyu
core +1 more source
Hyperspectral Mixed Noise Removal By
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 This article introduces a new hyperspectral image (HSI) denoising method that is able to cope with additive mixed noise, i.e., mixture of Gaussian noise, impulse noise, and stripes, which usually corrupt hyperspectral images in the acquisition process ...
Lina Zhuang, Michael K. Ng
doaj +1 more source