scholarly journals License Plate Image Reconstruction Based on Generative Adversarial Networks

2021 ◽  
Vol 13 (15) ◽  
pp. 3018
Author(s):  
Mianfen Lin ◽  
Liangxin Liu ◽  
Fei Wang ◽  
Jingcong Li ◽  
Jiahui Pan
Keyword(s):  
Image Reconstruction ◽  
Intelligent Transportation Systems ◽  
Structural Similarity ◽  
Input Image ◽  
Transportation Systems ◽  
Image Features ◽  
Generative Adversarial Networks ◽  
License Plate ◽  
Reconstruction Method ◽  
Adversarial Networks

License plate image reconstruction plays an important role in Intelligent Transportation Systems. In this paper, a super-resolution image reconstruction method based on Generative Adversarial Networks (GAN) is proposed. The proposed method mainly consists of four parts: (1) pretreatment for the input image; (2) image features extraction using residual dense network; (3) introduction of progressive sampling, which can provide larger receptive field and more information details; (4) discriminator based on markovian discriminator (PatchGAN) can make a more accurate judgment, which guides the generator to reconstruct images with higher quality and details. Regarding the Chinese City Parking Dataset (CCPD) dataset, compared with the current better algorithm, the experiment results prove that our model has a higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) and less reconstruction time, which verifies the feasibility of our approach.

scholarly journals SARA-GAN: Self-Attention and Relative Average Discriminator Based Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

2020 ◽  
Vol 14 ◽  
Author(s):  
Zhenmou Yuan ◽  
Mingfeng Jiang ◽  
Yaming Wang ◽  
Bo Wei ◽  
Yongming Li ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Attention Mechanism ◽  
Generative Adversarial Networks ◽  
Reconstruction Method ◽  
Mri Imaging ◽  
Adversarial Networks ◽  
Reconstruction Methods ◽  
Mri Reconstruction

Research on undersampled magnetic resonance image (MRI) reconstruction can increase the speed of MRI imaging and reduce patient suffering. In this paper, an undersampled MRI reconstruction method based on Generative Adversarial Networks with the Self-Attention mechanism and the Relative Average discriminator (SARA-GAN) is proposed. In our SARA-GAN, the relative average discriminator theory is applied to make full use of the prior knowledge, in which half of the input data of the discriminator is true and half is fake. At the same time, a self-attention mechanism is incorporated into the high-layer of the generator to build long-range dependence of the image, which can overcome the problem of limited convolution kernel size. Besides, spectral normalization is employed to stabilize the training process. Compared with three widely used GAN-based MRI reconstruction methods, i.e., DAGAN, DAWGAN, and DAWGAN-GP, the proposed method can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measure(SSIM), and the details of the reconstructed image are more abundant and more realistic for further clinical scrutinization and diagnostic tasks.

Shale Digital Core Image Generation Based on Generative Adversarial Networks

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Wenshu Zha ◽  
Xingbao Li ◽  
Daolun Li ◽  
Yan Xing ◽  
Lei He ◽  
...  
Keyword(s):  
Organic Matter ◽  
Morphological Characteristics ◽  
Generative Adversarial Networks ◽  
Reconstruction Method ◽  
Core Samples ◽  
Adversarial Networks ◽  
Training Samples ◽  
Digital Core ◽  
Artificial Intelligence Models ◽  
Convolution Properties

Abstract Stochastic reconstruction of digital core images is a vital part of digital core physics analysis, aiming to generate representative microstructure samples for sampling and uncertainty quantification analysis. This paper proposes a novel reconstruction method of the digital core of shale based on generative adversarial networks (GANs) with powerful capabilities of the generation of samples. GANs are a series of unsupervised generative artificial intelligence models that take the noise vector as an input. In this paper, the GANs with a generative and a discriminative network are created respectively, and the shale image with 45 nm/pixel preprocessed by the three-value-segmentation method is used as training samples. The generative network is used to learn the distribution of real training samples, and the discriminative network is used to distinguish real samples from synthetic ones. Finally, realistic digital core samples of shale are successfully reconstructed through the adversarial training process. We used the Fréchet inception distance (FID) and Kernel inception distance (KID) to evaluate the ability of GANs to generate real digital core samples of shale. The comparison of the morphological characteristics between them, such as the ratio of organic matter and specific surface area of organic matter, indicates that real and reconstructed samples are highly close. The results show that deep convolutional generative adversarial networks with full convolution properties can reconstruct digital core samples of shale effectively. Therefore, compared with the classical methods of reconstruction, the new reconstruction method is more promising.

scholarly journals A High-Dimensional Video Sequence Completion Method with Traffic Data Completion Generative Adversarial Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lan Wu ◽  
Tian Gao ◽  
Chenglin Wen ◽  
Kunpeng Zhang ◽  
Fanshi Kong
Keyword(s):  
Intelligent Transportation Systems ◽  
Spatial Information ◽  
Transportation Systems ◽  
Video Data ◽  
Generative Adversarial Networks ◽  
High Dimensional ◽  
Traffic Data ◽  
Adversarial Network ◽  
Time Space ◽  
Data Completion

The lack of traffic data is a bottleneck restricting the development of Intelligent Transportation Systems (ITS). Most existing traffic data completion methods aim at low-dimensional data, which cannot cope with high-dimensional video data. Therefore, this paper proposes a traffic data complete generation adversarial network (TDC-GAN) model to solve the problem of missing frames in traffic video. Based on the Feature Pyramid Network (FPN), we designed a multiscale semantic information extraction model, which employs a convolution mechanism to mine informative features from high-dimensional data. Moreover, by constructing a discriminator model with global and local branch networks, the temporal and spatial information are captured to ensure the time-space consistency of consecutive frames. Finally, the TDC-GAN model performs single-frame and multiframe completion experiments on the Caltech pedestrian dataset and KITTI dataset. The results show that the proposed model can complete the corresponding missing frames in the video sequences and achieve a good performance in quantitative comparative analysis.

scholarly journals Cell Morphology-Guided De Novo Hit Design by Conditioning Generative Adversarial Networks on Phenotypic Image Features

2020 ◽  
Author(s):  
Oscar Méndez-Lucio ◽  
Paula Andrea Marin Zapata ◽  
Joerg Wichard ◽  
David Rouquié ◽  
Djork-Arné Clevert
Keyword(s):  
Small Molecules ◽  
De Novo ◽  
Molecular Targets ◽  
De Novo Design ◽  
Image Features ◽  
Mitigation Strategy ◽  
Generative Adversarial Networks ◽  
Target Information ◽  
Adversarial Networks ◽  
First Time

Developing new small molecules that are bioactive is time-consuming, costly and rarely successful. As a mitigation strategy, we apply, for the first time, generative adversarial networks to de novo design of small molecules using a phenotype-based drug discovery approach. We trained our model on a set of 30,000 compounds and their respective morphological profiles extracted from high content images; no target information was used to train the model. Using this approach, we were able to automatically design agonist-like compounds of different molecular targets.

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