Scattering Removal for Finger-Vein Image Enhancement

2011 ◽  
Author(s):  
Jinfeng Yang ◽  
Ben Zhang
Keyword(s):  
Image Enhancement ◽  
Finger Vein

scholarly journals Finger-Vein Image Enhancement Using a Fuzzy-Based Fusion Method with Gabor and Retinex Filtering

Sensors ◽  
2014 ◽  
Vol 14 (2) ◽  
pp. 3095-3129 ◽  
Author(s):  
Kwang Shin ◽  
Young Park ◽  
Dat Nguyen ◽  
Kang Park
Keyword(s):  
Image Enhancement ◽  
Fusion Method ◽  
Finger Vein

scholarly journals Finger Vein Segmentation from Infrared Images Based on a Modified Separable Mumford Shah Model and Local Entropy Thresholding

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Marios Vlachos ◽  
Evangelos Dermatas
Keyword(s):  
Image Enhancement ◽  
Local Application ◽  
Local Entropy ◽  
Infrared Images ◽  
Vein Pattern ◽  
Finger Vein ◽  
Small Window ◽  
Minimization Procedure ◽  
Novel Method ◽  
Computationally Intensive

A novel method for finger vein pattern extraction from infrared images is presented. This method involves four steps: preprocessing which performs local normalization of the image intensity, image enhancement, image segmentation, and finally postprocessing for image cleaning. In the image enhancement step, an image which will be both smooth and similar to the original is sought. The enhanced image is obtained by minimizing the objective function of a modified separable Mumford Shah Model. Since, this minimization procedure is computationally intensive for large images, a local application of the Mumford Shah Model in small window neighborhoods is proposed. The finger veins are located in concave nonsmooth regions and, so, in order to distinct them from the other tissue parts, all the differences between the smooth neighborhoods, obtained by the local application of the model, and the corresponding windows of the original image are added. After that, veins in the enhanced image have been sufficiently emphasized. Thus, after image enhancement, an accurate segmentation can be obtained readily by a local entropy thresholding method. Finally, the resulted binary image may suffer from some misclassifications and, so, a postprocessing step is performed in order to extract a robust finger vein pattern.

scholarly journals Finger-Vein Image Enhancement Based on Pulse Coupled Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 57226-57237 ◽  
Author(s):  
Lei Lei ◽  
Feng Xi ◽  
Shengyao Chen
Keyword(s):  
Neural Network ◽  
Image Enhancement ◽  
Pulse Coupled Neural Network ◽  
Finger Vein

scholarly journals Finger Vein Image Enhancement Based on Guided Tri-Gaussian Filters

2021 ◽  
Vol 1 (1) ◽  
pp. 17-23
Author(s):  
Liping Zhang ◽  
Xinran Wang ◽  
Xiaoli Dong ◽  
Linjun Sun ◽  
Weiwei Cai ◽  
...  
Keyword(s):  
Receptive Field ◽  
Image Enhancement ◽  
Light Source ◽  
Gaussian Model ◽  
Input Image ◽  
Finger Movement ◽  
Vein Pattern ◽  
Finger Vein ◽  
Enhancement Method ◽  
Pattern Segmentation

In the process of image acquisition, the contrast between veins and non-veins in finger vein images is not high due to the influence of the fuzzy light source, skin scattering and finger movement. To solve this problem, a finger vein image enhancement method is proposed (GTGFs), which enhances finger vein patterns by setting guided image as input image firstly. On this basis, the tri-Gaussian model is based on disinhibitory properties of the concentric receptive field used to locally enhancing the image. The parameters of the tri-Gaussian model are determined based on the finger vein width information. The experiment results show that the proposed enhancement method can significantly enhance the finger vein patterns and improve the recognition effect of the methods based on vein pattern segmentation.

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