Vector anisotropic filter for multispectral image denoising

2015 ◽  
Author(s):  
Ahmed Ben Said ◽  
Sebti Foufou ◽  
Rachid Hadjidj
Keyword(s):  
Image Denoising ◽  
Multispectral Image ◽  
Anisotropic Filter

scholarly journals Multispectral image denoising using sparse and graph Laplacian Tucker decomposition

2020 ◽  
Vol 6 (3) ◽  
pp. 319-331
Author(s):  
Xiaoce Wu ◽  
Bingyin Zhou ◽  
Qingyun Ren ◽  
Wei Guo
Keyword(s):  
Prior Knowledge ◽  
Image Denoising ◽  
Graph Laplacian ◽  
Noise Removal ◽  
Multispectral Image ◽  
Low Rank ◽  
Tucker Decomposition ◽  
Spectral Bands ◽  
Global Correlation ◽  
Ill Posed

Abstract Multispectral image denoising is a basic problem whose results affect subsequent processes such as target detection and classification. Numerous approaches have been proposed, but there are still many challenges, particularly in using prior knowledge of multispectral images, which is crucial for solving the ill-posed problem of noise removal. This paper considers both non-local self-similarity in space and global correlation in spectrum. We propose a novel low-rank Tucker decomposition model for removing the noise, in which sparse and graph Laplacian regularization terms are employed to encode this prior knowledge. It can jointly learn a sparse and low-rank representation while preserving the local geometrical structure between spectral bands, so as to better capture simultaneously the correlation in spatial and spectral directions. We adopt the alternating direction method of multipliers to solve the resulting problem. Experiments demonstrate that the proposed method outperforms the state-of-the-art, such as cube-based and tensor-based methods, both quantitatively and qualitatively.

A method for remote sensing image denoising based on structured dictionary learning and cloud computing

2020 ◽  
pp. 002072092093107
Author(s):  
Ru Yang ◽  
Zhentao Qin ◽  
Xiangyu Zhao
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Image Denoising ◽  
Dictionary Learning ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Multispectral Image ◽  
Structured Dictionary

With the emerging technology of remote sensing, a huge amount of remote sensing data is collected and stored in the remote sensin02222g platform, and the transmission and processing of data on the platform is extremely wasteful. It is essential to incorporate the speedy remote sensing processing services in an integrated cloud computing architecture. In order to improve the denoising ability of remote sensing image, a new structured dictionary-based method for multispectral image denoising based on cluster is proposed. This method incorporates both the locality of spatial and the correlation across spectrum of multispectral image. Remote sensing image is divided into different groups by clustering, and sparse representation coefficients of spatial and spectral and dictionary is obtained according to the dictionary learning algorithm. After threshold processing, the similar blocks are averaged and realized with multispectral remote sensing image denoising. The algorithm is applied to denoise the noisy remote sensing image of Maoergai area in the upper Minjiang which contain typical vegetation and soil is chosen as study area, simulation results show that higher peak-signal to noise ratio can be obtained as compared to other recent image denoising methods.

A block-thresholding method for multispectral image denoising

2005 ◽  
Author(s):  
Caroline Chaux ◽  
Amel Benazza-Benyahia ◽  
Jean-Christophe Pesquet
Keyword(s):  
Image Denoising ◽  
Multispectral Image ◽  
Thresholding Method ◽  
Block Thresholding

Multispectral image denoising methods: A literature review

2020 ◽  
Vol 33 ◽  
pp. 4666-4670
Author(s):  
P. Lokeshwara Reddy ◽  
Santosh Pawar
Keyword(s):  
Literature Review ◽  
Image Denoising ◽  
Multispectral Image

scholarly journals Multispectral Image Denoising via Nonlocal Multitask Sparse Learning

2018 ◽  
Vol 10 (1) ◽  
pp. 116 ◽  
Author(s):  
Ya-Ru Fan ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Liang-Jian Deng ◽  
Shanxiong Fan
Keyword(s):  
Image Denoising ◽  
Multispectral Image ◽  
Sparse Learning
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