scholarly journals Optimal Graph Edge Weights Driven NLMs with Multi-layer Residual Compensation

2021 ◽  
Author(s):  
Fang Yang ◽  
Xin Chen ◽  
Li Chai
Keyword(s):  
Image Denoising ◽  
Graph Laplacian ◽  
Denoising Method ◽  
Local Means ◽  
Effective Performance ◽  
Non Local ◽  
Local Gradient ◽  
Laplacian Regularization ◽  
Image Details ◽  
Optimal Graph

Abstract Non-Local Means (NLMs) play important roles in image denoising, restoration, inpainting etc. due to its simple theory but effective performance. In this paper, in order to better remove noise without loss of image details, we further develop the NLMs based denoising method. It is realized by introducing a graph Laplacian regularization based weighting model and a multi-layer residual compensation strategy. Unlike the patch similarity based weights in classic NLMs filters, the graph Laplacian regularization defines the weights by considering 1) the distance between target pixel and the candidate pixel, 2) the local gradient and 3) the patch similarity. The proposed NLMs filter performs in a multi-layer framework to better remove the noise and smooth the result. The corresponding residuals at each layer are finally combined with the smooth image with learned weights to recover the image details. Experimental results show that our method is effective and robust, especially for piecewise-smooth images.

scholarly journals Optimal graph edge weights driven nlms with multi-layer residual compensation

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Fang Yang ◽  
Xin Chen ◽  
Li Chai
Keyword(s):  
Image Denoising ◽  
Graph Laplacian ◽  
Denoising Method ◽  
Local Means ◽  
Effective Performance ◽  
Non Local ◽  
Local Gradient ◽  
Laplacian Regularization ◽  
Image Details ◽  
Optimal Graph

AbstractNon-local Means (NLMs) play essential roles in image denoising, restoration, inpainting, etc., due to its simple theory but effective performance. However, when the noise increases, the denoising accuracy of NLMs decreases significantly. This paper further develop the NLMs-based denoising method to remove noise with less loss of image details. It is realized by embedding an optimal graph edge weights driven NLMs kernel into a multi-layer residual compensation framework. Unlike the patch similarity-based weights in the traditional NLMs filters, the edge weights derived from the optimal graph Laplacian regularization consider (1) the distance between the target pixel and the candidate pixel, (2) the local gradient and (3) the patch similarity. After defining the weights, the graph-based NLMs kernel is then put into a multi-layer framework. The corresponding primal and residual terms at each layer are finally fused with learned weights to recover the image. Experimental results show that our method is effective and robust, especially for piecewise smooth images.

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
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