Image Denoising and Inpainting Model Based on Taylor Expansion

2009 ◽  
Author(s):  
Xiaoli Sun ◽  
Chen Xu
Keyword(s):  
Image Denoising ◽  
Taylor Expansion ◽  
Model Based

scholarly journals An Improved Image Denoising Model Based on Nonlocal Means Filter

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yan Jin ◽  
Wenyu Jiang ◽  
Jianlong Shao ◽  
Jin Lu
Keyword(s):  
Image Denoising ◽  
Natural Images ◽  
Nonlocal Means ◽  
New Model ◽  
Model Based ◽  
Better Than

The nonlocal means filter plays an important role in image denoising. We propose in this paper an image denoising model which is a suitable improvement of the nonlocal means filter. We compare this model with the nonlocal means filter, both theoretically and experimentally. Experiment results show that this new model provides good results for image denoising. Particularly, it is better than the nonlocal means filter when we consider the denoising for natural images with high textures.

Directional-smoothing-model-based image denoising algorithm

2020 ◽  
Vol 87 (5) ◽  
pp. 299
Author(s):  
Xiaoming Zhao ◽  
Yashuo Bai ◽  
Xin Liu ◽  
Miao Gao ◽  
Kun Cheng ◽  
...  
Keyword(s):  
Image Denoising ◽  
Model Based

A New Regularization Model Based on Non-Local Means for Image Deblurring

2013 ◽  
Vol 411-414 ◽  
pp. 1164-1169 ◽  
Author(s):  
Zhi Ming Wang ◽  
Hong Bao
Keyword(s):  
Image Denoising ◽  
Processing Speed ◽  
Operator Splitting ◽  
Image Deblurring ◽  
Local Similarity ◽  
Model Based ◽  
Local Means ◽  
Ill Posed ◽  
Non Local ◽  
Local Image

Image deblurring with noise is a typical ill-posed problem needs regularization. Various regularization models were proposed during several decades study, such as Tikhonov and TV. A new regularization model based non-local similarity constrains is proposed in this paper, which used l2 non-local norms and could be easily solved by fast non-local image denoising algorithm. By combining with Bregmanrized operator splitting (BOS) algorithm, a fast and efficient iterative three step image deblurring scheme is given. Experimental results show that proposed regularization model obtained better results on ten common test images than other similar regularization model including newly proposed NLTV regularization, both in deblurring performance (PSNR and MSSIM) and processing speed.

Extended Shearlet HMT Model-Based Image Denoising Using BKF Distribution

2015 ◽  
Vol 54 (3) ◽  
pp. 301-319 ◽  
Author(s):  
Xiang-Yang Wang ◽  
Na Zhang ◽  
Hong-Liang Zheng ◽  
Yang-Cheng Liu
Keyword(s):  
Image Denoising ◽  
Model Based

An adaptive VPDE image denoising model based on structure tensor

2019 ◽  
Vol 78 (19) ◽  
pp. 28331-28354 ◽  
Author(s):  
Cong Jin ◽  
Qian Li ◽  
Shu-Wei Jin
Keyword(s):  
Image Denoising ◽  
Structure Tensor ◽  
Model Based

Iterative Regularization Model for Image Denoising Based on Dual Norms

2012 ◽  
Vol 182-183 ◽  
pp. 1245-1249
Author(s):  
Guan Nan Chen ◽  
Dan Er Xu ◽  
Rong Chen ◽  
Zu Fang Huang ◽  
Zhong Jian Teng
Keyword(s):  
Image Denoising ◽  
Iterative Procedure ◽  
Convergence Property ◽  
Energy Functional ◽  
Image Features ◽  
Experimental Results ◽  
Iterative Regularization ◽  
Model Based ◽  
Oscillating Patterns

Image denoising algorithm based on gradient dependent energy functional often compromised the image features like textures or certain details. This paper proposes an iterative regularization model based on Dual Norms for image denoising. By using iterative regularization model, the oscillating patterns of texture and detail are added back to fit and compute the original Dual Norms model, and the iterative behavior avoids overfull smoothing while denoising the features of textures and details to a certain extent. In addition, the iterative procedure is proposed in this paper, and the proposed algorithm also be proved the convergence property. Experimental results show that the proposed method can achieve a batter result in preserving not only the features of textures for image denoising but also the details for image.

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