Results 1 to 10 of about 539 (168)

Hyperspectral Denoising Using Asymmetric Noise Modeling Deep Image Prior

Remote Sensing, 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.
Yifan Wang, Shuang Xu, Xiangyong Cao, Qiao Ke, Teng-Yu Ji, Xiangxiang Zhu   +5 more
doaj   +2 more sources

How Hyperspectral Image Unmixing and Denoising Can Boost Each Other

Remote Sensing, 2020
Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the
Behnood Rasti, Bikram Koirala, Paul Scheunders, Pedram Ghamisi   +3 more
doaj   +2 more sources

Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation

Remote Sensing, 2021
Hyperspectral image (HSI) is easily corrupted by different kinds of noise, such as stripes, dead pixels, impulse noise, Gaussian noise, etc. Due to less consideration of the structural specificity of stripes, many existing HSI denoising methods cannot ...
Fang Yang, Xin Chen, Li Chai
doaj   +2 more sources

Revolutionizing hyper spectral image denoising: a squeezenet paradigm [PDF]

Scientific Reports
Hyperspectral images (HSIs) frequently experience various types of noise due to atmospheric interference and sensor instability, which impairs the efficiency of subsequent operations.
Nandhagopal Nachimuthu, Ramya Murugesan, M. Dharmalingam, G. Prakash   +3 more
doaj   +2 more sources

Hyperspectral Image Denoising via Framelet Transformation Based Three-Modal Tensor Nuclear Norm

Remote Sensing, 2021
During the acquisition process, hyperspectral images (HSIs) are inevitably contaminated by mixed noise, which seriously affects the image quality. To improve the image quality, HSI denoising is a critical preprocessing step.
Wenfeng Kong, Yangyang Song, Jing Liu
doaj   +2 more sources

Hyperspectral Image Denoising via Adversarial Learning

Remote Sensing, 2022
Due to sensor instability and atmospheric interference, hyperspectral images (HSIs) often suffer from different kinds of noise which degrade the performance of downstream tasks.
Junjie Zhang, Zhouyin Cai, Fansheng Chen, Dan Zeng   +3 more
doaj   +2 more sources

Moreau-Enhanced Total Variation and Subspace Factorization for Hyperspectral Denoising

Remote Sensing, 2020
Hyperspectral images (HSIs) denoising aims at recovering noise-free images from noisy counterparts to improve image visualization. Recently, various prior knowledge has attracted much attention in HSI denoising, e.g., total variation (TV), low-rank ...
Yanhong Yang, Shengyong Chen, Jianwei Zheng   +2 more
doaj   +2 more sources

Robust Hyperspectral Unmixing with Practical Learning-Based Hyperspectral Image Denoising

Remote Sensing, 2023
The noise corruption problem commonly exists in hyperspectral images (HSIs) and severely affects the accuracy of hyperspectral unmixing algorithms.
Risheng Huang, Xiaorun Li, Yiming Fang, Zeyu Cao, Chaoqun Xia   +4 more
doaj   +2 more sources

Hyperspectral Image Denoising via Low-Rank Tucker Decomposition with Subspace Implicit Neural Representation

Remote Sensing
Hyperspectral image (HSI) denoising is an important preprocessing step for downstream applications. Fully characterizing the spatial-spectral priors of HSI is crucial for denoising tasks.
Cheng Cheng, Dezhi Sun, Yaoyuan Yang, Zhoucheng Guo, Jiangjun Peng   +4 more
doaj   +2 more sources

Hyperspectral Image Mixed Denoising via Robust Representation Coefficient Image Guidance and Nonlocal Low-Rank Approximation

Remote Sensing
Recently, hyperspectral image (HSI) mixed denoising methods based on nonlocal subspace representation (NSR) have achieved significant success. However, most of these methods focus on optimizing the denoiser for representation coefficient images (RCIs ...
Jiawei Song, Baolong Guo, Zhe Yuan, Chao Wang, Fangliang He, Cheng Li   +5 more
doaj   +2 more sources