Hyperspectral Image Denoising using Dictionary Learning [PDF]
2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2019 Hyperspectral images are corrupted by noise during their acquisition. In this work, we propose to efficiently denoise hyperspectral images under two assumptions: (i) noiseless hyperspectral images in matrix form are low-rank, and (ii) image patches are sparse in a proper representation domain defined through a dictionary.
Dantas, Cassio +2 more
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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [PDF]
, 2012 Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras.
Paul Gader +13 more
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A multivariate wavelet-PCA denoising-filter for hyperspectral images
, 2022 S.205-208In this paper we investigate the use of multivariate multiresolution principal component analysis for filtering and denoising of signals. From the proposed model we deduce several properties that particularly address the properties of hyper ...
Bollenbeck, F. +2 more
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AI-Enabled Imaging for Pathogen Detection Under Stress Conditions: A Systematic Review. [PDF]
Compr Rev Food Sci Food SafABSTRACT Advances in pathogen detection that incorporate artificial intelligence (AI) may capture microbial signals under challenging environmental conditions that traditional methods miss. This systematic review evaluates the application, performance, and methodological characteristics of AI‐enabled imaging for pathogen detection, including its impact
Papa M, Kuehnle G, Oh YJE, Yi J.
europepmc +2 more sources
Superpixel-level Graph Feature Learning Method for Hyperspectral Image Denoising [PDF]
Jisuanji kexueHyperspectral image denoising methods based on traditional deep learning usually have difficulty capturing the long-range correlation of spatial positions and the similarity of global irregular local blocks,resulting in loss of detailed information and ...
WU Ying, YE Hailiang, CAO Feilong
doaj +1 more source
A Neural Network for Hyperspectral Image Denoising by Combining Spatial–Spectral Information
Remote Sensing, 2023 Hyperspectral imaging often suffers from various types of noise, including sensor non-uniformity and atmospheric disturbances. Removing multiple types of complex noise in hyperspectral images (HSIs) while preserving high fidelity in spectral dimensions ...
Xiaoying Lian +6 more
doaj +1 more source
Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation [PDF]
, 2011 Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying ...
Alzubi, S +5 more
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Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing
Frontiers in Plant Science, 2021 This paper focuses on image segmentation, image correction and spatial-spectral dimensional denoising of images in hyperspectral image preprocessing to improve the classification accuracy of hyperspectral images.
Yu-hang Li +15 more
doaj +1 more source
Learning Deep Dictionary for Hyperspectral Image Denoising
IEICE Transactions on Information and Systems, 2015 Using traditional single-layer dictionary learning methods, it is difficult to reveal the complex structures hidden in the hyperspectral images. Motivated by deep learning technique, a deep dictionary learning approach is proposed for hyperspectral image denoising, which consists of hierarchical dictionary learning, feature denoising and fine-tuning ...
HUO, Leigang +3 more
openaire +2 more sources
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