Image denoising with dual-directional filter bank GSM model and non-local mean filter

2014 ◽  
Vol 22 (10) ◽  
pp. 2806-2814
Author(s):  
刘巧红 LIU Qiao-hong ◽  
李斌 LI Bin ◽  
林敏 LIN Min
Keyword(s):  
Image Denoising ◽  
Filter Bank ◽  
Directional Filter Bank ◽  
Directional Filter ◽  
Mean Filter ◽  
Local Mean ◽  
Non Local

scholarly journals Multispectral image denoising with optimized vector non-local mean filter

2016 ◽  
Vol 58 ◽  
pp. 115-126 ◽  
Author(s):  
Ahmed Ben Said ◽  
Rachid Hadjidj ◽  
Kamal Eddine Melkemi ◽  
Sebti Foufou
Keyword(s):  
Image Denoising ◽  
Multispectral Image ◽  
Mean Filter ◽  
Local Mean ◽  
Non Local

New Directional Filter Bank and its Application in Image Denoising

2010 ◽  
Vol 39 (2) ◽  
pp. 380-384
Author(s):  
周祚峰 CAO Jian-zhong ◽  
曹剑中 ZHOU Zuo-feng ◽  
程源源 CHENG Yuan-yuan ◽  
郭永明 GUO Yong-ming
Keyword(s):  
Image Denoising ◽  
Filter Bank ◽  
Directional Filter Bank ◽  
Directional Filter

scholarly journals Non-local mean filtering algorithm based on deep learning

2018 ◽  
Vol 232 ◽  
pp. 03025
Author(s):  
Baozhong Liu ◽  
Jianbin Liu
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Denoising Method ◽  
Filtering Algorithm ◽  
Local Mean ◽  
Non Local ◽  
Reduction Effect ◽  
Mean Filtering

Aimed at the problem that the traditional image denoising algorithm is not effective in noise reduction, a new image denoising method is proposed. The method combines deep learning and non-local mean filtering algorithms to denoise the noisy image to obtain better noise reduction effect. By comparing with Gaussian filtering algorithm, median filtering algorithm, bilateral filtering algorithm and early non-local mean filtering algorithm, the noise reduction effect of the new algorithm is better than the traditional method and the peak signal to noise ratio is compared with the early non-local mean algorithm. The performance is better.

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