scholarly journals Superresolution reconstruction method for ancient murals based on the stable enhanced generative adversarial network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jianfang Cao ◽  
Yiming Jia ◽  
Minmin Yan ◽  
Xiaodong Tian
Keyword(s):  
Network Model ◽  
Subjective Evaluation ◽  
Input Image ◽  
Image Features ◽  
Subjective Perception ◽  
Reconstruction Method ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Public Data ◽  
Feature Information

AbstractA stable enhanced superresolution generative adversarial network (SESRGAN) algorithm was proposed in this study to address the low-resolution and blurred texture details in ancient murals. This algorithm makes improvements on the basis of GANs, which use dense residual blocks to extract image features. After two upsampling steps, the feature information of the image is input into the high-resolution (HR) image space to realize an improvement in resolution, and the reconstructed HR image is finally generated. The discriminator network uses VGG as its basic framework to judge the authenticity of the input image. This study further optimized the details of the network model. In addition, three loss optimization models, i.e., the perceptual loss, content loss, and adversarial loss models, were integrated into the proposed algorithm. The Wasserstein GAN-gradient penalty (WGAN-GP) theory was used to optimize the adversarial loss of the model when calculating the perceptual loss and when using the preactivation feature information for calculation purposes. In addition, public data sets were used to pretrain the generative network model to achieve a high-quality initialization. The simulation experiment results showed that the proposed algorithm outperforms other related superresolution algorithms in terms of both objective and subjective evaluation indicators. A subjective perception evaluation was also conducted, and the reconstructed images produced by our algorithm were more in line with the general public’s visual perception than those produced by the other compared algorithms.

scholarly journals Classification of Space Objects by Using Deep Learning with Micro-Doppler Signature Images

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4365
Author(s):  
Kwangyong Jung ◽  
Jae-In Lee ◽  
Nammoon Kim ◽  
Sunjin Oh ◽  
Dong-Wook Seo
Keyword(s):  
Transfer Learning ◽  
Data Augmentation ◽  
Doppler Frequency ◽  
Extraction Methods ◽  
Classification Performance ◽  
Reconstruction Method ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Space Objects ◽  
Target Characteristics

Radar target classification is an important task in the missile defense system. State-of-the-art studies using micro-doppler frequency have been conducted to classify the space object targets. However, existing studies rely highly on feature extraction methods. Therefore, the generalization performance of the classifier is limited and there is room for improvement. Recently, to improve the classification performance, the popular approaches are to build a convolutional neural network (CNN) architecture with the help of transfer learning and use the generative adversarial network (GAN) to increase the training datasets. However, these methods still have drawbacks. First, they use only one feature to train the network. Therefore, the existing methods cannot guarantee that the classifier learns more robust target characteristics. Second, it is difficult to obtain large amounts of data that accurately mimic real-world target features by performing data augmentation via GAN instead of simulation. To mitigate the above problem, we propose a transfer learning-based parallel network with the spectrogram and the cadence velocity diagram (CVD) as the inputs. In addition, we obtain an EM simulation-based dataset. The radar-received signal is simulated according to a variety of dynamics using the concept of shooting and bouncing rays with relative aspect angles rather than the scattering center reconstruction method. Our proposed model is evaluated on our generated dataset. The proposed method achieved about 0.01 to 0.39% higher accuracy than the pre-trained networks with a single input feature.

scholarly journals Selective Feature Anonymization for Privacy-Preserving Image Data Publishing

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 874
Author(s):  
Taehoon Kim ◽  
Jihoon Yang
Keyword(s):  
Image Data ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Strong Positive Correlation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Public Data ◽  
Synthetic Datasets ◽  
Independent Features ◽  
Original Class

There is a strong positive correlation between the development of deep learning and the amount of public data available. Not all data can be released in their raw form because of the risk to the privacy of the related individuals. The main objective of privacy-preserving data publication is to anonymize the data while maintaining their utility. In this paper, we propose a privacy-preserving semi-generative adversarial network (PPSGAN) that selectively adds noise to class-independent features of each image to enable the processed image to maintain its original class label. Our experiments on training classifiers with synthetic datasets anonymized with various methods confirm that PPSGAN shows better utility than other conventional methods, including blurring, noise-adding, filtering, and generation using GANs.

scholarly journals Measuring Traffic Volumes Using an Autoencoder with No Need to Tag Images with Labels

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 702
Author(s):  
Seungbin Roh ◽  
Johyun Shin ◽  
Keemin Sohn
Keyword(s):  
Input Image ◽  
Video Frame ◽  
Generative Adversarial Network ◽  
Detection Algorithms ◽  
Adversarial Network ◽  
Simpler Algorithm ◽  
Proposed Model ◽  
Traffic Volumes ◽  
Step Algorithm ◽  
Almost All

Almost all vision technologies that are used to measure traffic volume use a two-step procedure that involves tracking and detecting. Object detection algorithms such as YOLO and Fast-RCNN have been successfully applied to detecting vehicles. The tracking of vehicles requires an additional algorithm that can trace the vehicles that appear in a previous video frame to their appearance in a subsequent frame. This two-step algorithm prevails in the field but requires substantial computation resources for training, testing, and evaluation. The present study devised a simpler algorithm based on an autoencoder that requires no labeled data for training. An autoencoder was trained on the pixel intensities of a virtual line placed on images in an unsupervised manner. The last hidden node of the former encoding portion of the autoencoder generates a scalar signal that can be used to judge whether a vehicle is passing. A cycle-consistent generative adversarial network (CycleGAN) was used to transform an original input photo of complex vehicle images and backgrounds into a simple illustration input image that enhances the performance of the autoencoder in judging the presence of a vehicle. The proposed model is much lighter and faster than a YOLO-based model, and accuracy of the proposed model is equivalent to, or better than, a YOLO-based model. In measuring traffic volumes, the proposed approach turned out to be robust in terms of both accuracy and efficiency.

scholarly journals Single Image Super-Resolution: Depthwise Separable Convolution Super-Resolution Generative Adversarial Network

2020 ◽  
Vol 10 (1) ◽  
pp. 375 ◽  
Author(s):  
Zetao Jiang ◽  
Yongsong Huang ◽  
Lirui Hu
Keyword(s):  
Similarity Index ◽  
Super Resolution ◽  
Structural Similarity ◽  
Image Features ◽  
Single Image ◽  
Generative Adversarial Network ◽  
Visual Evaluation ◽  
Adversarial Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution

The super-resolution generative adversarial network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied by unpleasant artifacts. To further enhance the visual quality, we propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The method is based on depthwise separable convolution super-resolution generative adversarial network (DSCSRGAN). A new depthwise separable convolution dense block (DSC Dense Block) was designed for the generator network, which improved the ability to represent and extract image features, while greatly reducing the total amount of parameters. For the discriminator network, the batch normalization (BN) layer was discarded, and the problem of artifacts was reduced. A frequency energy similarity loss function was designed to constrain the generator network to generate better super-resolution images. Experiments on several different datasets showed that the peak signal-to-noise ratio (PSNR) was improved by more than 3 dB, structural similarity index (SSIM) was increased by 16%, and the total parameter was reduced to 42.8% compared with the original model. Combining various objective indicators and subjective visual evaluation, the algorithm was shown to generate richer image details, clearer texture, and lower complexity.

scholarly journals Insulator Segmentation for Power Line Inspection Based on Modified Conditional Generative Adversarial Network

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Zishu Gao ◽  
Guodong Yang ◽  
En Li ◽  
Tianyu Shen ◽  
Zhe Wang ◽  
...  
Keyword(s):  
Transmission Lines ◽  
Power Supply ◽  
Power Transmission ◽  
State Of The Art ◽  
Power Line ◽  
Power Transmission Lines ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Adversarial Network ◽  
Feature Information

There are a large number of insulators on the transmission line, and insulator damage will have a major impact on power supply security. Image-based segmentation of the insulators in the power transmission lines is a premise and also a critical task for power line inspection. In this paper, a modified conditional generative adversarial network for insulator pixel-level segmentation is proposed. The generator is reconstructed by encoder-decoder layers with asymmetric convolution kernel which can simplify the network complexity and extract more kinds of feature information. The discriminator is composed of a fully convolutional network based on patchGAN and learns the loss to train the generator. It is verified in experiments that the proposed method has better performances on mIoU and computational efficiency than Pix2pix, SegNet, and other state-of-the-art networks.

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