Hyperspectral image denoising: From model-driven, data-driven, to model-data-driven
Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications.
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …
Image restoration for remote sensing: Overview and toolbox
Remote sensing provides valuable information about objects and areas from a distance in
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …
Zero-shot hyperspectral sharpening
Fusing hyperspectral images (HSIs) with multispectral images (MSIs) of higher spatial
resolution has become an effective way to sharpen HSIs. Recently, deep convolutional …
resolution has become an effective way to sharpen HSIs. Recently, deep convolutional …
Beyond supervised learning in remote sensing: A systematic review of deep learning approaches
An increasing availability of remote sensing data in the era of geo big-data makes producing
well-represented, reliable training data to be more challenging and requires an excessive …
well-represented, reliable training data to be more challenging and requires an excessive …
Spectrum-aware and transferable architecture search for hyperspectral image restoration
Convolutional neural networks have been widely developed for hyperspectral image (HSI)
restoration. However, making full use of the spatial-spectral information of HSIs still remains …
restoration. However, making full use of the spatial-spectral information of HSIs still remains …
Hyperspectral image restoration with self-supervised learning: A two-stage training approach
Hyperspectral image (HSI) denoising is a crucial preprocessing task to improve the
performance of the subsequent HSI interpretation and applications. With recent progress in …
performance of the subsequent HSI interpretation and applications. With recent progress in …
Hyperspectral image denoising using SURE-based unsupervised convolutional neural networks
HV Nguyen, MO Ulfarsson… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Hyperspectral images (HSIs) are useful for many remote sensing applications. However,
they are usually affected by noise that degrades the HSIs quality. Therefore, HSI denoising …
they are usually affected by noise that degrades the HSIs quality. Therefore, HSI denoising …
Hybrid spectral denoising transformer with guided attention
In this paper, we present a Hybrid Spectral Denoising Transformer (HSDT) for hyperspectral
image denoising. Challenges in adapting transformer for HSI arise from the capabilities to …
image denoising. Challenges in adapting transformer for HSI arise from the capabilities to …
Partial-DNet: A novel blind denoising model with noise intensity estimation for HSI
Y Yuan, H Ma, G Liu - IEEE Transactions on Geoscience and …, 2021 - ieeexplore.ieee.org
Because of the inevitable noise interference in hyperspectral images (HSIs), the
understanding and application of HSIs are seriously restricted. To solve this problem, the …
understanding and application of HSIs are seriously restricted. To solve this problem, the …
Model-based image signal processors via learnable dictionaries
Digital cameras transform sensor RAW readings into RGB images by means of their Image
Signal Processor (ISP). Computational photography tasks such as image denoising and …
Signal Processor (ISP). Computational photography tasks such as image denoising and …