Results 51 to 60 of about 1,639 (207)
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
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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
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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
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Tensor Robust CUR for Compression and Denoising of Hyperspectral Data
IEEE Access, 2023 Hyperspectral images are often contaminated with noise which degrades the quality of data. Recently, tensor robust principal component analysis (TRPCA) has been utilized to remove noise from hyperspectral images, improving classification accuracy ...
Mohammad M. Salut, David V. Anderson
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Multitask Sparse Neural Network for Hyperspectral Image Denoising
, 2022 Data-driven deep learning (DL)-based methods directly learn the nonlinear mapping between noisy hyperspectral images (HSIs) and corresponding clean ones.
Xiong, F, Zhou, J, Ye, M, Lu, J, Qian, Y
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Denoising Hyperspectral Image With Non-i.i.d. Noise Structure [PDF]
IEEE Transactions on Cybernetics, 2018 13 pages, 14 ...
Yang Chen 0057 +4 more
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Applied SciencesThe inhomogeneity of spectral pixel response is an unavoidable phenomenon in hyperspectral imaging, which is mainly manifested by the existence of inhomogeneity banding noise in the acquired hyperspectral data.
Jiayue Yan +6 more
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Effective feature extraction and data reduction with hyperspectral imaging in remote sensing
, 2014 Although PCA has been widely used for feature extraction and data reduction, it suffers from three main drawbacks: high computational cost, large memory requirement and low efficacy in processing large datasets such as HSI.
Zabalza, Jaime +3 more
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Unsupervised Hyperspectral Denoising Based on Deep Image Prior and Least Favorable Distribution
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 This article considers the inverse problem under hyperspectral images (HSIs) denoising framework. Recently, it has been shown that deep learning is a promising approach to image denoising.
Keivan Faghih Niresi, Chong-Yung Chi
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Advanced Science, EarlyView.Mid‐infrared photothermal imaging enables multidimensional profiling of micro‐ and nanoplastics in bottled water. A total of 9.9 × 104 particles L−1 is detected, with 64% in the nanoscale regime. Spectral evolution, including peak narrowing and band shifts, reveals local chain reorganization in polyethylene terephthalate (PET), highlighting intrinsic ...
Xinyu Deng +4 more
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