Results 21 to 30 of about 539 (168)
Hyperspectral Imaging Techniques for Lyophilization: Advances in Data-Driven Modeling Strategies and Applications. [PDF]
Adv Sci (Weinh)Lyophilization is a key process used in the production of biotherapeutic products. This article reviews and discusses the application of HSI on lyophilization, and the strategies that use the resulting data to build models. It is intended to provide guidance and insights for non‐specialist researchers and engineers into leveraging HSI and the data ...
Yu H +5 more
europepmc +2 more sources
Hyperspectral Mixed Noise Removal via Spatial-Spectral Constrained Unsupervised Deep Image Prior
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Recently, deep learning-based methods are proposed for hyperspectral images (HSIs) denoising. Among them, unsupervised methods such as deep image prior (DIP)-based methods have received much attention because these methods do not require any training ...
Yi-Si Luo +4 more
doaj +1 more source
PatchMask: A Data Augmentation Strategy with Gaussian Noise in Hyperspectral Images
Remote Sensing, 2022 Data augmentation (DA) is an effective way to enrich the richness of data and improve a model’s generalization ability. It has been widely used in many advanced vision tasks (e.g., classification, recognition, etc.), while it can hardly be seen in ...
Hong-Xia Dou +5 more
doaj +1 more source
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
HYPERSPECTRAL IMAGE DENOISING USING A NONLOCAL SPECTRAL SPATIAL PRINCIPAL COMPONENT ANALYSIS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classification and so on.
D. Li, L. Xu, J. Peng, J. Ma
doaj +1 more source
Iterative Refinement Network for Hyperspectral Image Denoising
, 2023 Hyperspectral image (HSI) denoising is an important pre-processing procedure for subsequent tasks. Learning a direct mapping from the observed noisy HSI to its clean counterpart is challenging, especially in the case of very severe noise.
Xiong, F, Zhou, J, Zhao, Z, Qian, Y
core +1 more source
Deep Parameterized Neural Networks for Hyperspectral Image Denoising
, 2023 Sparse representation (SR)-based hyperspectral image (HSI) denoising methods normally average the local denoising results of multiple overlapped cubes to recover the whole HSI.
Jiantao Zhou +8 more
core +1 more source
Nonlocal Spatial–Spectral Neural Network for Hyperspectral Image Denoising
, 2022 Hyperspectral image (HSI) denoising is an essential preprocessing step to improve the quality of HSIs. The difficulty of HSI denoising lies in effectively modeling the intrinsic characteristics of HSIs, such as spatial-spectral correlation (SSC), global ...
Zhou, Jun +4 more
core +1 more source
Local Low-Rank and Sparse Representation for Hyperspectral Image Denoising
IEEE Access, 2019 Hyperspectral image (HSI) denoising is a fundamental task in a plethora of HSI applications. Global low-rank property is widely adopted to exploit the spectral-spatial information of HSIs, providing satisfactory denoising results.
Guanqun Ma +3 more
doaj +1 more source
IEEE Transactions on Geoscience and Remote SensingThis paper introduces a novel paradigm for hyperspectral image (HSI) denoising, which is termed \textit{pan-denoising}. In a given scene, panchromatic (PAN) images capture similar structures and textures to HSIs but with less noise.
Shuang Xu, Qiao Ke, Jiangjun Peng
exaly +2 more sources