From Noise Modeling to Blind Image Denoising

2016 ◽  
Author(s):  
Fengyuan Zhu ◽  
Guangyong Chen ◽  
Pheng Ann Heng
Keyword(s):  
Image Denoising ◽  
Noise Modeling ◽  
Blind Image

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 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

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

scholarly journals Remote Sensing Image Denoising via Low-Rank Tensor Approximation and Robust Noise Modeling

2020 ◽  
Vol 12 (8) ◽  
pp. 1278 ◽  
Author(s):  
Tian-Hui Ma ◽  
Zongben Xu ◽  
Deyu Meng
Keyword(s):  
Remote Sensing ◽  
Image Denoising ◽  
Fundamental Problem ◽  
Remote Sensing Image ◽  
Noise Removal ◽  
Low Rank ◽  
Denoising Method ◽  
Noise Modeling ◽  
Rank Tensor ◽  
Tensor Approximation

Noise removal is a fundamental problem in remote sensing image processing. Most existing methods, however, have not yet attained sufficient robustness in practice, due to more or less neglecting the intrinsic structures of remote sensing images and/or underestimating the complexity of realistic noise. In this paper, we propose a new remote sensing image denoising method by integrating intrinsic image characterization and robust noise modeling. Specifically, we use low-Tucker-rank tensor approximation to capture the global multi-factor correlation within the underlying image, and adopt a non-identical and non-independent distributed mixture of Gaussians (non-i.i.d. MoG) assumption to encode the statistical configurations of the embedded noise. Then, we incorporate the proposed image and noise priors into a full Bayesian generative model and design an efficient variational Bayesian algorithm to infer all involved variables by closed-form equations. Moreover, adaptive strategies for the selection of hyperparameters are further developed to make our algorithm free from burdensome hyperparameter-tuning. Extensive experiments on both simulated and real multispectral/hyperspectral images demonstrate the superiority of the proposed method over the compared state-of-the-art ones.

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