scholarly journals Improvement to Blind Image Denoising by Using Local Pixel Grouping with SVD

2016 ◽  
Vol 79 ◽  
pp. 314-320 ◽  
Author(s):  
Rachana Dhannawat ◽  
Archana B. Patankar
Keyword(s):  
Image Denoising ◽  
Pixel Grouping ◽  
Blind Image

Laser Active Image-Denoising Based on Principal Component Analysis with Local Pixel Grouping

2014 ◽  
Vol 571-572 ◽  
pp. 753-756
Author(s):  
Wei Li Li ◽  
Xiao Qing Yin ◽  
Bin Wang ◽  
Mao Jun Zhang ◽  
Ke Tan
Keyword(s):  
Principal Component Analysis ◽  
Image Denoising ◽  
Principal Component ◽  
Speckle Noise ◽  
Component Analysis ◽  
Median Filtering ◽  
Denoising Method ◽  
Structure Information ◽  
Pixel Grouping ◽  
Low Dimensional

Denoising is an important issue for laser active image. This paper attempted to process laser active image in the low-dimensional sub-space. We adopted the principal component analysis with local pixel grouping (LPG-PCA) denoising method proposed by Zhang [1], and compared it with the conventional denoising method for laser active image, such as wavelet filtering, wavelet soft threshold filtering and median filtering. Experimental results show that the image denoised by LPG-PCA has higher BIQI value than other images, most of the speckle noise can be reduced and the detail structure information is well preserved. The low-dimensional sub-space idea is a new direction for laser active image denoising.

scholarly journals Attention-based Pyramid Dilated Lattice Network for Blind Image Denoising

2021 ◽  
Author(s):  
Mohammad Nikzad ◽  
Yongsheng Gao ◽  
Jun Zhou
Keyword(s):  
Image Denoising ◽  
Contextual Information ◽  
Test Time ◽  
Noise Levels ◽  
Single Model ◽  
Dilated Convolution ◽  
Effectiveness And Efficiency ◽  
Lattice Network ◽  
Blind Image ◽  
Different Levels

Though convolutional neural networks (CNNs) with residual and dense aggregations have obtained much attention in image denoising, they are incapable of exploiting different levels of contextual information at every convolutional unit in order to infer different levels of noise components with a single model. In this paper, to overcome this shortcoming we present a novel attention-based pyramid dilated lattice (APDL) architecture and investigate its capability for blind image denoising. The proposed framework can effectively harness the advantages of residual and dense aggregations to achieve a great trade-off between performance, parameter efficiency, and test time. It also employs a novel pyramid dilated convolution strategy to effectively capture contextual information corresponding to different noise levels through the training of a single model. Our extensive experimental investigation verifies the effectiveness and efficiency of the APDL architecture for image denoising as well as JPEG artifacts suppression tasks.

scholarly journals Two-stage image denoising by principal component analysis with local pixel grouping

2010 ◽  
Vol 43 (4) ◽  
pp. 1531-1549 ◽  
Author(s):  
Lei Zhang ◽  
Weisheng Dong ◽  
David Zhang ◽  
Guangming Shi
Keyword(s):  
Principal Component Analysis ◽  
Image Denoising ◽  
Principal Component ◽  
Component Analysis ◽  
Two Stage ◽  
Pixel Grouping

scholarly journals Blind Image Denoising via Dependent Dirichlet Process Tree

2017 ◽  
Vol 39 (8) ◽  
pp. 1518-1531 ◽  
Author(s):  
Fengyuan Zhu ◽  
Guangyong Chen ◽  
Jianye Hao ◽  
Pheng-Ann Heng
Keyword(s):  
Image Denoising ◽  
Dirichlet Process ◽  
Dependent Dirichlet Process ◽  
Blind Image ◽  
Process Tree
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