On the influence of anisotropic diffusion filter on dorsal hand authentication using eigenveins

2017 ◽  
Vol 29 (4) ◽  
pp. 1507-1528
Author(s):  
Sarah Hachemi-Benziane ◽  
Abdelkader Benyettou
Keyword(s):  
Anisotropic Diffusion ◽  
Diffusion Filter ◽  
Anisotropic Diffusion Filter ◽  
Dorsal Hand

An anisotropic diffusion filter based on multidirectional separability

2013 ◽  
Author(s):  
Shen Liu ◽  
Jianguo Wei ◽  
Xin Wang ◽  
Wenhuan Lu ◽  
Qiang Fang ◽  
...  
Keyword(s):  
Anisotropic Diffusion ◽  
Diffusion Filter ◽  
Anisotropic Diffusion Filter

Anisotropic diffusion based denoising on concrete images and surface crack segmentation

2019 ◽  
Vol 11 (3) ◽  
pp. 395-409
Author(s):  
Diana Andrushia ◽  
N. Anand ◽  
Prince Arulraj
Keyword(s):  
Health Monitoring ◽  
Anisotropic Diffusion ◽  
Structural Damage ◽  
Performance Metrics ◽  
Research Work ◽  
Structural Similarity ◽  
Structural Defects ◽  
Content Type ◽  
Diffusion Filter ◽  
Anisotropic Diffusion Filter

Purpose Health monitoring of concrete is one of the important tasks in the structural health monitoring. The life of any infrastructure relies on the quality of the concrete. The computer vision-based methods are very useful to identify the structural defects. The identification of minor cracks in the noisy concrete image is complex. The purpose of this paper is to denoise the concrete crack images and also segment the cracks. Design/methodology/approach The novelty of the proposed work lies on the usage of anisotropic diffusion filter in the noisy concrete images. Initially anisotropic diffusion filter is applied to smoothen the concrete images. Adaptive threshold and gray level-based edge stopping constant are used in the diffusion process. The statistical six sigma-based method is utilized to segment the cracks from smoothened concrete images. Findings The proposed method is compared with five state-of-the-art-methods with the performance metrics of mean square error, peak signal to noise ratio and mean structural similarity. The experimental results highlight the advantages of the proposed method. Originality/value The novelty of the proposed work lies on the usage of anisotropic diffusion filter in the noisy concrete images. This research work gives the scope for structural damage evaluation by the automation techniques.

Weighted Image Averaging Based Anisotropic Diffusion Denoising Method for Ultrasound Thyroid Image

2020 ◽  
Vol 10 (2) ◽  
pp. 380-390
Author(s):  
Haiyue Zhang ◽  
Daoyun Xu ◽  
Yongbin Qin
Keyword(s):  
Thyroid Disease ◽  
Anisotropic Diffusion ◽  
Averaging Method ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Denoising Method ◽  
Weighted Image ◽  
Diffusion Filter ◽  
Anisotropic Diffusion Filter ◽  
Image Averaging

Thyroid disease is a frequent occurrence in clinical practice and the computerized analysis of ultrasonography has been becoming the most prospective tool for thyroid disease automatic diagnosis. However, the accuracy of vision-based diagnostic analysis is often reduced because the quality of ultrasound image is easily corrupted by the speckle noise. Thus, noise suppression is imperative and significant for the thyroid ultrasonography image preprocessing to increase the reliability of subsequent analysis. In this paper, we propose a novel weighted image averaging method based on anisotropic diffusion filters combination to remove speckle noise and enhance the details of the image at the same time. The method first denoises the image separately by two filters with different performances. The speckle reducing anisotropic diffusion filter can enhance the details of the image, and the anisotropic diffusion filter can better suppress the speckle noise in the image. In order to integrate the advantages of the two filters and reduce the mutual interference meanwhile, an adaptive weighted image averaging method is further proposed to combine the pixels of the two denoised images. The experimental results indicate that the proposed method can achieve promising performance on the template images with various noise levels by considering PSNR and SSIM. What's more, it is not only superior to other methods in automatic segmentation, but also can obtain better visual effect for thyroid images.

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