End-to-End Generative Adversarial Network for Palm-Vein Recognition

2020 ◽  
pp. 714-724
Author(s):  
Huafeng Qin ◽  
Mounîm A. El Yacoubi
Keyword(s):  
Generative Adversarial Network ◽  
Adversarial Network ◽  
End To End ◽  
Vein Recognition ◽  
Palm Vein

scholarly journals Modified Conditional Generative Adversarial Network-Based Optical Blur Restoration for Finger-Vein Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 16281-16301 ◽  
Author(s):  
Jiho Choi ◽  
Kyoung Jun Noh ◽  
Se Woon Cho ◽  
Se Hyun Nam ◽  
Muhammad Owais ◽  
...  
Keyword(s):  
Generative Adversarial Network ◽  
Finger Vein ◽  
Adversarial Network ◽  
Vein Recognition ◽  
Optical Blur ◽  
Finger Vein Recognition

scholarly journals EnsNet: Ensconce Text in the Wild

2019 ◽  
Vol 33 ◽  
pp. 801-808 ◽  
Author(s):  
Shuaitao Zhang ◽  
Yuliang Liu ◽  
Lianwen Jin ◽  
Yaoxiong Huang ◽  
Songxuan Lai
Keyword(s):  
Generative Adversarial Network ◽  
Local Consistency ◽  
Adversarial Network ◽  
Image Patches ◽  
Scene Text ◽  
In The Wild ◽  
Lateral Connection ◽  
Previous State ◽  
End To End ◽  
General Object

A new method is proposed for removing text from natural images. The challenge is to first accurately localize text on the stroke-level and then replace it with a visually plausible background. Unlike previous methods that require image patches to erase scene text, our method, namely ensconce network (EnsNet), can operate end-to-end on a single image without any prior knowledge. The overall structure is an end-to-end trainable FCN-ResNet-18 network with a conditional generative adversarial network (cGAN). The feature of the former is first enhanced by a novel lateral connection structure and then refined by four carefully designed losses: multiscale regression loss and content loss, which capture the global discrepancy of different level features; texture loss and total variation loss, which primarily target filling the text region and preserving the reality of the background. The latter is a novel local-sensitive GAN, which attentively assesses the local consistency of the text erased regions. Both qualitative and quantitative sensitivity experiments on synthetic images and the ICDAR 2013 dataset demonstrate that each component of the EnsNet is essential to achieve a good performance. Moreover, our EnsNet can significantly outperform previous state-of-the-art methods in terms of all metrics. In addition, a qualitative experiment conducted on the SBMNet dataset further demonstrates that the proposed method can also preform well on general object (such as pedestrians) removal tasks. EnsNet is extremely fast, which can preform at 333 fps on an i5-8600 CPU device.

Evaluation of end-to-end CNN models for palm vein recognition

2021 ◽  
Author(s):  
Jose I. Santamaria ◽  
Ruber Hernandez-Garcia ◽  
Ricardo J. Barrientos ◽  
Francisco Manuel Castro ◽  
Julian Ramos-Cozar ◽  
...  
Keyword(s):  
End To End ◽  
Vein Recognition ◽  
Palm Vein

ToStaGAN: An End-to-End Two-Stage Generative Adversarial Network for Brain Tumor Segmentation

2021 ◽  
Author(s):  
Yi Ding ◽  
Chao Zhang ◽  
Mingsheng Cao ◽  
Yilei Wang ◽  
Dajiang Chen ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Two Stage ◽  
Generative Adversarial Network ◽  
Brain Tumor Segmentation ◽  
Adversarial Network ◽  
End To End

scholarly journals End-to-End Unpaired Image Denoising with Conditional Adversarial Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 4140-4149
Author(s):  
Zhiwei Hong ◽  
Xiaocheng Fan ◽  
Tao Jiang ◽  
Jianxing Feng
Keyword(s):  
Image Denoising ◽  
Real Data ◽  
Training Data ◽  
Noise Distribution ◽  
Data Types ◽  
Generative Adversarial Network ◽  
Noisy Images ◽  
Adversarial Network ◽  
Noisy Observation ◽  
End To End

Image denoising is a classic low level vision problem that attempts to recover a noise-free image from a noisy observation. Recent advances in deep neural networks have outperformed traditional prior based methods for image denoising. However, the existing methods either require paired noisy and clean images for training or impose certain assumptions on the noise distribution and data types. In this paper, we present an end-to-end unpaired image denoising framework (UIDNet) that denoises images with only unpaired clean and noisy training images. The critical component of our model is a noise learning module based on a conditional Generative Adversarial Network (cGAN). The model learns the noise distribution from the input noisy images and uses it to transform the input clean images to noisy ones without any assumption on the noise distribution and data types. This process results in pairs of clean and pseudo-noisy images. Such pairs are then used to train another denoising network similar to the existing denoising methods based on paired images. The noise learning and denoising components are integrated together so that they can be trained end-to-end. Extensive experimental evaluation has been performed on both synthetic and real data including real photographs and computer tomography (CT) images. The results demonstrate that our model outperforms the previous models trained on unpaired images as well as the state-of-the-art methods based on paired training data when proper training pairs are unavailable.

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