Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

He, Jing; Shen, Lianfeng; Yao, Yu-Dong; Wang, Huaxia; Zhao, Guodong; Gu, Xiaowei; Ding, Weiping

doi:10.1109/tetci.2021.3097734

Cited by 10 publications

(2 citation statements)

References 29 publications

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“…Choi [29] constructed a lightweight adversarial neural network (GAN) for enhancing finger vein images to achieve image deblurring. He et al [30] proposed an improved GAN method in which, by adjusting the loss weights, the contrast between vein and non-vein regions is improved. It is effective in recovering the optical blur in original finger vein images.…”

Section: Finger Vein Image Enhancement Based On Deep Featurementioning

confidence: 99%

A Degraded Finger Vein Image Recovery and Enhancement Algorithm Based on Atmospheric Scattering Theory

Feng,

Mao

et al. 2024

Sensors

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With the development of biometric identification technology, finger vein identification has received more and more widespread attention for its security, efficiency, and stability. However, because of the performance of the current standard finger vein image acquisition device and the complex internal organization of the finger, the acquired images are often heavily degraded and have lost their texture characteristics. This makes the topology of the finger veins inconspicuous or even difficult to distinguish, greatly affecting the identification accuracy. Therefore, this paper proposes a finger vein image recovery and enhancement algorithm using atmospheric scattering theory. Firstly, to normalize the local over-bright and over-dark regions of finger vein images within a certain threshold, the Gamma transform method is improved in this paper to correct and measure the gray value of a given image. Then, we reconstruct the image based on atmospheric scattering theory and design a pixel mutation filter to segment the venous and non-venous contact zones. Finally, the degraded finger vein images are recovered and enhanced by global image gray value normalization. Experiments on SDUMLA-HMT and ZJ-UVM datasets show that our proposed method effectively achieves the recovery and enhancement of degraded finger vein images. The image restoration and enhancement algorithm proposed in this paper performs well in finger vein recognition using traditional methods, machine learning, and deep learning. The recognition accuracy of the processed image is improved by more than 10% compared to the original image.

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Section: Finger Vein Image Enhancement Based On Deep Featurementioning

confidence: 99%

A Degraded Finger Vein Image Recovery and Enhancement Algorithm Based on Atmospheric Scattering Theory

Feng,

Mao

et al. 2024

Sensors

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show abstract

“…In addition to successful applications in texture classification [4] and face recognition [5], the LBP technique has also exhibited satisfactory results in different domains of image processing. These include dynamic texture [6], medical image analysis [7], object detection [8], image segmentation [9], fingerprint detection [10], color texture [11], finger vein image restoration [12], and others. Due to its simple principle, rapid computation, low storage requirements, high efficiency, and rotational invariance, researchers have proposed many LBPbased variant methods.…”

Section: Introductionmentioning

confidence: 99%

Texture Classification Method Based on Local Enhancement and Non-local Median Patterns

Lian,

Dong

2024

AJST

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Local binary pattern (LBP) serves as a highly powerful method for texture classification. LBP and its variant methods are extensively applied across various domains of image processing.In increasingly complex imaging environments, LBP faces two issues: (1) Loss of detail information during feature extraction. (2) Sensitivity to noise. To mitigate these issues, this paper proposes a method called Local Enhancement and Non-local Median Pattern (LENMP). It consists of two operators: Local Adaptive Contrast Enhancement Pattern (LEP) and Non-local Median Binary Pattern (NMBP). The LEP operator captures the sign and magnitude details of local image characteristics, while the NMBP operator captures the global information of image features. First, the image is processed using the threshold ACE (thACE) algorithm to enhance the contrast of high-frequency information in the image, extracting image contour edge information. Then, median extraction is performed separately on the two operators to capture larger spatial texture detail information. Finally, comparative experiments are conducted on multiple datasets (Outex, CUReT, Brodatz, KTH-TIPS, and Kylberg) with representative texture classification methods. The results indicate that our proposed LENMP texture classification method exhibits good classification performance and remains competitive compared to the latest descriptors.

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Image blind deblurring networks with back-projection feature fusion

Kong

Xue

et al. 2023

SIViP

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： Aiming at the problem of image motion blur caused by hand-held camera jitter and object motion in the process of collecting photos, a generative adversarial network (GAN) based on feature fusion of back projection is proposed for blind image deblurring. Firstly, the generator network is established by using U-Net structure, and a feature fusion residual block based on back projection is designed according to the error feedback principle, which solves the problem of saving spatial information in U-Net structure. Secondly, the self-attention module is introduced into the generator network to extract the feature map that pays more attention to detail. Finally, the combination of perceptual loss, mean square error loss and relative generative adversarial loss effectively alleviates the mode collapse problem of traditional GAN and improves the stability of model training. The experimental results show that the peak signal to noise ratio (PSNR) and structural similarity (SSIM) of this method on GoPro data set are 30.183dB and 0.941 respectively, and 26.962 and 0.837 on the Kohler dataset, with the shortest running time, which are better than the existing mainstream methods. The restored image is clearer in subjective vision and richer in texture details, which can effectively improve the image deblurring effect.

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Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

Cited by 10 publications

References 29 publications

A Degraded Finger Vein Image Recovery and Enhancement Algorithm Based on Atmospheric Scattering Theory

A Degraded Finger Vein Image Recovery and Enhancement Algorithm Based on Atmospheric Scattering Theory

Texture Classification Method Based on Local Enhancement and Non-local Median Patterns

Image blind deblurring networks with back-projection feature fusion

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