Image denoising based on non-subsampled shearlet transform use non-local means and hard threshold

2019 ◽  
Author(s):  
Jing Liu ◽  
Yuesong Li ◽  
Jianhui Ge
Keyword(s):  
Image Denoising ◽  
Shearlet Transform ◽  
Local Means ◽  
Non Local

Automated Image Denoising Model: Contribution Towards Optimized Internal and External Basis

2021 ◽  
pp. 2150051
Author(s):  
S. Elavaar Kuzhali ◽  
D. S. Suresh
Keyword(s):  
Image Denoising ◽  
Image Patch ◽  
The Past ◽  
Local Means ◽  
Hybrid Optimization Algorithm ◽  
Proposed Model ◽  
Processing Step ◽  
Non Local ◽  
Chicken Swarm Optimization ◽  
Selection Of

For handling digital images for various applications, image denoising is considered as a fundamental pre-processing step. Diverse image denoising algorithms have been introduced in the past few decades. The main intent of this proposal is to develop an effective image denoising model on the basis of internal and external patches. This model adopts Non-local means (NLM) for performing the denoising, which uses redundant information of the image in pixel or spatial domain to reduce the noise. While performing the image denoising using NLM, “denoising an image patch using the other noisy patches within the noisy image is done for internal denoising and denoising a patch using the external clean natural patches is done for external denoising”. Here, the selection of optimal block from the entire datasets including internal noisy images and external clean natural images is decided by a new hybrid optimization algorithm. The two renowned optimization algorithms Chicken Swarm Optimization (CSO), and Dragon Fly Algorithm (DA) are merged, and the new hybrid algorithm Rooster-based Levy Updated DA (RLU-DA) is adopted. The experimental results in terms of some relevant performance measures show the promising results of the proposed model with remarkable stability and high accuracy.

scholarly journals Brain MR image denoising for Rician noise using pre-smooth non-local means filter

2015 ◽  
Vol 14 (1) ◽  
pp. 2 ◽  
Author(s):  
Jian Yang ◽  
Jingfan Fan ◽  
Danni Ai ◽  
Shoujun Zhou ◽  
Songyuan Tang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Rician Noise ◽  
Mr Image ◽  
Local Means ◽  
Non Local

scholarly journals Synthetic-Aperture Radar Image Despeckling based on Improved Non-Local Means and Non-Subsampled Shearlet Transform

2020 ◽  
Vol 49 (3) ◽  
pp. 299-307
Author(s):  
Zengguo Sun ◽  
Rui Shi ◽  
Wei Wei
Keyword(s):  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Sar Images ◽  
Shearlet Transform ◽  
Local Means ◽  
New Type ◽  
Non Local ◽  
Homogeneous Regions ◽  
Transform Domains

When Synthetic-Aperture (SAR) image is transformed into wavelet domain and other transform domains, most of the coefficients of the image are small or zero. This shows that SAR image is sparse. However, speckle can be seen in SAR images. The non-local means is a despeckling algorithm, but it cannot overcome the speckle in homogeneous regions and it blurs edge details of the image. In order to solve these problems, an improved non-local means is suggested. At the same time, in order to better suppress the speckle effectively in edge regions, the non-subsampled Shearlet transform (NSST) is applied. By combining NSST with the improved non-local means, a new type of despeckling algorithm is proposed. Results show that the proposed algorithm leads to a satisfying performance for SAR images.

A Novel Method with Adaptive Non-local Means for Image Denoising

2012 ◽  
Vol 4 (16) ◽  
pp. 251-257
Author(s):  
Wenhui Li ◽  
Bo Fu ◽  
Chunri Cui ◽  
Huiying Li ◽  
Ying Wang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Local Means ◽  
Novel Method ◽  
Non Local
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