An Adaptive Image Denoising Model Based on Nonlocal Diffusion Tensor

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.

Download Full-text

Direct diffusion tensor estimation using a model-based method with spatial and parametric constraints

Medical Physics ◽

10.1002/mp.12054 ◽

2017 ◽

Vol 44 (2) ◽

pp. 570-580 ◽

Cited By ~ 5

Author(s):

Yanjie Zhu ◽

Xi Peng ◽

Yin Wu ◽

Ed X. Wu ◽

Leslie Ying ◽

...

Keyword(s):

Diffusion Tensor ◽

Model Based

Download Full-text

A Texture Image Denoising Model Based on Image Frequency and Energy Minimization

Lecture Notes in Electrical Engineering - Proceedings of the 2012 International Conference on Information Technology and Software Engineering ◽

10.1007/978-3-642-34531-9_101 ◽

2012 ◽

pp. 939-949

Author(s):

Chanjuan Liu ◽

Xu Qian ◽

Caixia Li

Keyword(s):

Image Denoising ◽

Energy Minimization ◽

Texture Image ◽

Model Based ◽

Image Frequency

Download Full-text

Student’s-t Mixture Model Based Image Denoising Method with Gradient Fidelity Term

Advances in Computer Science and Ubiquitous Computing - Lecture Notes in Electrical Engineering ◽

10.1007/978-981-10-3023-9_49 ◽

2016 ◽

pp. 306-311

Author(s):

J. W. Zhang ◽

J. Liu ◽

Y. H. Zheng ◽

J. Wang

Keyword(s):

Image Denoising ◽

Mixture Model ◽

Denoising Method ◽

Model Based ◽

Fidelity Term ◽

Student’S T

Download Full-text

Fast Nonlocal Diffusion by Stacking Local Filters for Image Denoising

Communications in Computer and Information Science - Data Science ◽

10.1007/978-981-15-0121-0_6 ◽

2019 ◽

pp. 90-101

Author(s):

Peng Qiao ◽

Weichu Sun ◽

Yong Dou ◽

Rongchun Li

Keyword(s):

Image Denoising ◽

Nonlocal Diffusion ◽

Local Filters

Download Full-text

The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules

Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs ◽

10.4018/978-1-4666-1755-1.ch017 ◽

2012 ◽

pp. 227-235 ◽

Cited By ~ 1

Author(s):

D. Selvathi ◽

S. Thamarai Selvi ◽

C. Loorthu Sahaya Malar

Keyword(s):

Image Denoising ◽

Mean Squared Error ◽

Diffusion Tensor ◽

Curvelet Transform ◽

Research Area ◽

Magnetic Resonance Images ◽

Image Domain ◽

Active Research ◽

Unbiased Risk ◽

Wavelet Image

SURE-LET Approach is used for reducing or removing noise in brain Magnetic Resonance Images (MRI). Removing or reducing noise is an active research area in image processing. Rician noise is the dominant noise in MRIs. Due to this type of noise, the abnormal tissue (cancerous tissue) may be misclassified as normal tissue and introduces bias into MRI measurements that can have significant impact on the shapes and orientations of tensors in diffusion tensor MRIs. SURE is a new approach to Orthonormal wavelet image denoising. It is an image-domain minimization of an estimate of the mean squared error—Stein’s unbiased risk estimates (SURE). Here, the denoising process can be expressed as a linear combination of elementary denoising processes-linear expansion of thresholds (LET). Different Shrinkage functions such as Soft and Hard and Shrinkage rules and Universal and BayesShrink are used to remove noise and the performance of these results are compared. The algorithm is applied on brain MRIs with different noisy conditions by varying standard deviation of noise. The performance of this approach is compared with performance of the Curvelet transform.

Download Full-text

Directional-smoothing-model-based image denoising algorithm

Journal of Optical Technology ◽

10.1364/jot.87.000299 ◽

2020 ◽

Vol 87 (5) ◽

pp. 299

Author(s):

Xiaoming Zhao ◽

Yashuo Bai ◽

Xin Liu ◽

Miao Gao ◽

Kun Cheng ◽

...

Keyword(s):

Image Denoising ◽

Model Based

Download Full-text

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.1164 ◽

2013 ◽

Vol 411-414 ◽

pp. 1164-1169 ◽

Cited By ~ 1

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.

Download Full-text