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Zero-Shot Hyperspectral Image Denoising With Separable Image Prior
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019Supervised learning with a convolutional neural network is recognized as a powerful means of image restoration. However, most such methods have been designed for application to grayscale and/or color images; therefore, they have limited success when applied to hyperspectral image restoration. This is partially owing to large datasets being difficult to
Ryuji Imamura +2 more
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Hyperspectral Image Denoising with Spectrum Alignment
Proceedings of the 31st ACM International Conference on Multimedia, 2023Jiahua Xiao, Yantao Ji, Xing Wei 0001
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Hyperspectral image denoising via sparsity and low rank
2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013Hyperspectral noise is unavoidable in capture and transmission process, and it will degrade the detection and classification performance greatly. Noise free signal can be approximated using few atom or basis, while noisy signal is not. There are lots of similar spatial-spectral structures in noise free hyperspectral image.
Yongqiang Zhao 0001, Jinxiang Yang
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Denoising in hyperspectral images by superpixel based unmixing
2016 24th Signal Processing and Communication Application Conference (SIU), 2016Integration of spatial information into spectral processing is well-known to provide significant performance enhancement for tasks such as classification or unmixing. Recently, superpixel segmentation approaches have gained attention in the literature because they integrate spatial information efficiently into the spectral tasks, and have been utilized
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Hyperspectral Image Denoising by Fusing the Selected Related Bands
IEEE Transactions on Geoscience and Remote Sensing, 2019Hyperspectral images (HSIs) convey more useful information than RGB or gray images, which are widely used in many remote sensing tasks. In real scenarios, HSIs are inevitably corrupted by noise because of sensors’ imperfectness or atmospheric influence. Recently, many HSI denoising methods have been proposed to utilize the interband information between
Xiangtao Zheng +2 more
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Hyperspectral Image Denoising with Discrete Cosine Transform and CNN Denoiser
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023Lingsheng Wu +2 more
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Bayesian framework selection for hyperspectral image denoising
Signal Processing, 2022Tahereh Bahraini +3 more
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Spectral–Spatial Kernel Regularized for Hyperspectral Image Denoising
IEEE Transactions on Geoscience and Remote Sensing, 2015Noise contamination is a ubiquitous problem in hyperspectral images (HSIs), which is a challenging and promising theme in many remote sensing applications. A large number of methods have been proposed to remove noise. Unfortunately, most denoising methods fail to take full advantages of the high spectral correlation and to simultaneously consider the
Yuan Yuan 0001 +2 more
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Simultaneous Denoising and Intrinsic Order Selection in Hyperspectral Imaging
IEEE Transactions on Geoscience and Remote Sensing, 2011In this paper, we address the problem of order selection in noisy hyperspectral applications. In conventional unmixing methods, this problem has been divided into two separate processes of order selection and unmixing. Order selection methods generally use a denoising approach at the beginning stage.
Masoud Farzam, Soosan Beheshti
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Guided Hyperspectral Image Denoising with Realistic Data
International Journal of Computer Vision, 2022Tao Zhang 0042 +2 more
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