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Low-exposure vein image enhancement method based on cross-scale feature fusion

A vein image and feature fusion technology, applied in the field of computer vision, can solve the problems of vein detail information loss, unclear vein veins, low image contrast, etc., and achieve the effect of enhancing the ability of representation learning

Inactive Publication Date: 2021-08-17
CHINA UNIV OF MINING & TECH
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  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to provide a low-exposure vein image enhancement method based on cross-scale feature fusion, which can effectively solve the problems of dark vein image background, low image contrast, unclear vein veins and loss of vein detail information

Method used

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  • Low-exposure vein image enhancement method based on cross-scale feature fusion
  • Low-exposure vein image enhancement method based on cross-scale feature fusion
  • Low-exposure vein image enhancement method based on cross-scale feature fusion

Examples

Experimental program
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Embodiment 1

[0046] Step 1. Collect 2000 images of veins on the back of the human hand under normal exposure, and convert the images of veins on the back of the human hand under normal exposure into low-exposure images of the back of the hand one by one through the low-brightness image synthesis method. 4000 vein images with a pixel size of 256×256, 4000 vein images are used as a training set, such as figure 2 shown;

[0047] Step 2. Use the cross-scale feature fusion module to replace the convolutional layer in the basic residual module, and add an attention mechanism to obtain the vein cross-scale fusion residual block; according to the required image enhancement effect (image evaluation index: structural consistency SSIM and peak signal-to-noise ratio (PSNR), construct several vein cross-scale fusion residual blocks;

[0048] The three scale branches of the image are obtained through the local maximum pooling method to obtain low-level input features. Each branch uses the same convolu...

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Abstract

The invention discloses a low-exposure vein image enhancement method based on cross-scale feature fusion. The method comprises the following steps: making a low-exposure vein image data set, and constructing a cross-scale feature fusion module; based on a channel attention mechanism, constructing a vein cross-scale fusion residual block by adopting a residual structure; connecting and stacking a plurality of residual blocks end to end to form a vein image cross-scale fusion model, constructing a test set, and inputting the low-exposure vein image in the test set into the vein image cross-scale fusion model to obtain an enhanced vein image. According to the multi-scale branch cross-scale feature information fusion method provided by the invention, spatial structure information between different scales of the vein image is fully utilized, the representation learning ability of the network model for detail information such as vein veins is enhanced, and the enhancement effect of the low-exposure vein image is improved.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a low-exposure vein image enhancement method based on cross-scale feature fusion. Background technique [0002] Compared with other biometric features (such as fingerprint, iris, gesture and face), vein blood has the characteristics of anti-counterfeiting and easy acceptance, and has become one of the most popular personal identification methods. However, due to the limitations of vein acquisition equipment, the collected images often have problems such as dark background, low image contrast, unclear vein veins and loss of vein information details. [0003] Vein recognition requires more vein detail information, and traditional low-exposure image enhancement algorithms cannot solve the problem of vein information loss. Lv et al. proposed a multi-branch low-light enhancement network. The network loss function involves the structure, context and regional information of the image, wh...

Claims

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Application Information

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IPC IPC(8): G06T5/50G06K9/62G06N3/04G06N3/08
CPCG06T5/50G06N3/08G06T2207/30101G06T2207/20221G06T2207/20024G06T2207/20081G06T2207/20084G06N3/045G06F18/253
Inventor 王军韩淑雨潘在宇李玉莲申政文陈晓玲
Owner CHINA UNIV OF MINING & TECH
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