Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Hyperspectral image denoising"'
Autor:
Haitao Yin, Hao Chen
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 4125-4138 (2024)
Despite the significant successes in hyperspectral image (HSI) denoising, pure data-driven HSI denoising networks still suffer from limited understanding of inference. Deep unfolding (DU) is a feasible way to improve the interpretability of deep netw
Externí odkaz:
https://doaj.org/article/a64d117c91ac47ad85b4282a042798bc
Publikováno v:
Remote Sensing, Vol 16, Iss 15, p 2694 (2024)
Model-based hyperspectral image (HSI) denoising methods have attracted continuous attention in the past decades, due to their effectiveness and interpretability. In this work, we aim at advancing model-based HSI denoising, through sophisticated inves
Externí odkaz:
https://doaj.org/article/d9f2f22cc93e4cf096edfe876a1d8174
Autor:
Chen Chushen
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
In this paper, based on tensor decomposition, SSTV regular constraints are combined with low-rank 3D tensor for image denoising and the effect of the algorithm is enhanced by the augmented Lagrangian method to construct a hyperspectral image denoisin
Externí odkaz:
https://doaj.org/article/a223099de4cf4d3bb60aff631f3abd08
Publikováno v:
Remote Sensing, Vol 16, Iss 12, p 2071 (2024)
To address the problems of noise interference and image blurring in hyperspectral imaging (HSI), this paper proposes a denoising method for HSI based on deep learning and a total variation (TV) prior. The method minimizes the first-order moment dista
Externí odkaz:
https://doaj.org/article/da94fe970ac34a08b487018d37b6aedf
Publikováno v:
IEEE Access, Vol 11, Pp 91082-91099 (2023)
Hyperspectral image denoising is an important research topic in the field of remote sensing image processing. Recently, methods based on non-local low-rank tensor approximation have gained widespread attention towing to their ability to fully exploit
Externí odkaz:
https://doaj.org/article/62825c362752442dab0c9c5540088a1e
Autor:
Mohammad M. Salut, David V. Anderson
Publikováno v:
IEEE Access, Vol 11, Pp 77492-77505 (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. Ho
Externí odkaz:
https://doaj.org/article/c280aeed36ea4464b0fec8f27f120fc4
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 6693-6710 (2023)
The quality of hyperspectral images seriously impedes subsequent high-level vision tasks such as image segmentation, image encoding, and target detection. However, the frequency, spectral, and spatial properties of the hyperspectral noise pictures ar
Externí odkaz:
https://doaj.org/article/9b0885b5a42e4d609305f9bfc62ec2e3
Akademický článek
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Akademický článek
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Publikováno v:
Remote Sensing, Vol 15, Iss 8, p 1970 (2023)
Deep image prior (DIP) is a powerful technique for image restoration that leverages an untrained network as a handcrafted prior. DIP can also be used for hyperspectral image (HSI) denoising tasks and has achieved impressive performance. Recent works
Externí odkaz:
https://doaj.org/article/79a35f03e93f4e7fa21d482a1d37b001