scholarly journals Adaptive Wavelet Filter With Edge Compensation for Remote Sensing Image Denoising

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 91966-91979
Author(s):  
Boyang Chen ◽  
Xuan Feng ◽  
Ronghua Wu ◽  
Qiang Guo ◽  
Xi Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Denoising ◽  
Remote Sensing Image ◽  
Wavelet Filter ◽  
Adaptive Wavelet

Dual-Complementary Convolution Network for Remote-Sensing Image Denoising

2021 ◽  
pp. 1-5
Author(s):  
Xinlei Jia ◽  
Yali Peng ◽  
Jun Li ◽  
Bao Ge ◽  
Yunhong Xin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Denoising ◽  
Remote Sensing Image

Optimal wavelet filter design for remote sensing image compression

2007 ◽  
Vol 24 (2) ◽  
pp. 276-284 ◽  
Author(s):  
Guoan Yang ◽  
Nanning Zheng ◽  
Shugang Guo
Keyword(s):  
Remote Sensing ◽  
Image Compression ◽  
Filter Design ◽  
Remote Sensing Image ◽  
Wavelet Filter

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.

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