scholarly journals Video Super-Resolution Based on Generative Adversarial Network and Edge Enhancement

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 459
Author(s):  
Jialu Wang ◽  
Guowei Teng ◽  
Ping An
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Visual Experience ◽  
Super Resolution ◽  
Ground Truth ◽  
Intermediate Result ◽  
Edge Enhancement ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Sr Reconstruction

With the help of deep neural networks, video super-resolution (VSR) has made a huge breakthrough. However, these deep learning-based methods are rarely used in specific situations. In addition, training sets may not be suitable because many methods only assume that under ideal circumstances, low-resolution (LR) datasets are downgraded from high-resolution (HR) datasets in a fixed manner. In this paper, we proposed a model based on Generative Adversarial Network (GAN) and edge enhancement to perform super-resolution (SR) reconstruction for LR and blur videos, such as closed-circuit television (CCTV). The adversarial loss allows discriminators to be trained to distinguish between SR frames and ground truth (GT) frames, which is helpful to produce realistic and highly detailed results. The edge enhancement function uses the Laplacian edge module to perform edge enhancement on the intermediate result, which helps further improve the final results. In addition, we add the perceptual loss to the loss function to obtain a higher visual experience. At the same time, we also tried training network on different datasets. A large number of experiments show that our method has advantages in the Vid4 dataset and other LR videos.

scholarly journals Frontal Cortex Segmentation of Brain PET Imaging Using Deep Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Qianyi Zhan ◽  
Yuanyuan Liu ◽  
Yuan Liu ◽  
Wei Hu
Keyword(s):  
Neural Networks ◽  
Frontal Cortex ◽  
Pet Imaging ◽  
Deep Neural Networks ◽  
Ground Truth ◽  
Generative Adversarial Network ◽  
Learning Framework ◽  
Adversarial Network ◽  
Brain Pet ◽  
Positron Emission

18F-FDG positron emission tomography (PET) imaging of brain glucose use and amyloid accumulation is a research criteria for Alzheimer's disease (AD) diagnosis. Several PET studies have shown widespread metabolic deficits in the frontal cortex for AD patients. Therefore, studying frontal cortex changes is of great importance for AD research. This paper aims to segment frontal cortex from brain PET imaging using deep neural networks. The learning framework called Frontal cortex Segmentation model of brain PET imaging (FSPET) is proposed to tackle this problem. It combines the anatomical prior to frontal cortex into the segmentation model, which is based on conditional generative adversarial network and convolutional auto-encoder. The FSPET method is evaluated on a dataset of 30 brain PET imaging with ground truth annotated by a radiologist. Results that outperform other baselines demonstrate the effectiveness of the FSPET framework.

scholarly journals Fast and accurate learned multiresolution dynamical downscaling for precipitation

2021 ◽  
Author(s):  
Jiali Wang ◽  
Zhengchun Liu ◽  
Ian Foster ◽  
Won Chang ◽  
Rajkumar Kettimuthu ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Dynamical Downscaling ◽  
Computational Cost ◽  
Super Resolution ◽  
Ground Truth ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Spatial And Temporal Distributions ◽  
Data Variability

Abstract. This study develops a neural network-based approach for emulating high-resolution modeled precipitation data with comparable statistical properties but at greatly reduced computational cost. The key idea is to use combination of low- and high- resolution simulations to train a neural network to map from the former to the latter. Specifically, we define two types of CNNs, one that stacks variables directly and one that encodes each variable before stacking, and we train each CNN type both with a conventional loss function, such as mean square error (MSE), and with a conditional generative adversarial network (CGAN), for a total of four CNN variants.We compare the four new CNN-derived high-resolution precipitation results with precipitation generated from original high resolution simulations, a bilinear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique. Results show that the SR technique produces results similar to those of the bilinear interpolator with smoother spatial and temporal distributions and smaller data variabilities and extremes than the high resolution simulations. While the new CNNs trained by MSE generate better results over some regions than the interpolator and SR technique do, their predictions are still not as close as ground truth. The CNNs trained by CGAN generate more realistic and physically reasonable results, better capturing not only data variability in time and space but also extremes such as intense and long-lasting storms. The new proposed CNN-based downscaling approach can downscale precipitation from 50 km to 12 km in 14 min for 30 years once the network is trained (training takes 4 hours using 1 GPU), while the conventional dynamical downscaling would take 1 months using 600 CPU cores to generate simulations at the resolution of 12 km over contiguous United States.

scholarly journals Generative Enhancement of 3D Image Classifiers

2020 ◽  
Vol 10 (21) ◽  
pp. 7433
Author(s):  
Michal Varga ◽  
Ján Jadlovský ◽  
Slávka Jadlovská
Keyword(s):  
Neural Networks ◽  
Knowledge Sharing ◽  
Classification Accuracy ◽  
Network Architecture ◽  
Deep Neural Networks ◽  
3D Image ◽  
Accuracy Improvement ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Generative Modeling

In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the advantages of both non-generative classifiers and generative modeling. Its purpose is to streamline the synthesis of novel deep neural networks by embedding existing compatible classifiers into a generative network architecture. A demonstration of this process and evaluation of its effectiveness is performed using a 3D convolutional classifier and its generative equivalent—a 3D conditional generative adversarial network classifier. The results of the experiments show that the generative classifier delivers higher performance, gaining a relative classification accuracy improvement of 7.43%. An increase of accuracy is also observed when comparing it to a plain convolutional classifier that was trained on a dataset augmented with samples created by the trained generator. This suggests a desirable knowledge sharing mechanism exists within the hybrid discriminator-classifier network.

scholarly journals Edge Loss for Remote Sensing Image Super-Resolution

2021 ◽  
Author(s):  
Jiaoyue Li ◽  
Weifeng Liu ◽  
Kai Zhang ◽  
Baodi Liu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Super Resolution ◽  
Ground Truth ◽  
Remote Sensing Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Sensing Applications ◽  
Image Super Resolution ◽  
Edge Based

Remote sensing image super-resolution (SR) plays an essential role in many remote sensing applications. Recently, remote sensing image super-resolution methods based on deep learning have shown remarkable performance. However, directly utilizing the deep learning methods becomes helpless to recover the remote sensing images with a large number of complex objectives or scene. So we propose an edge-based dense connection generative adversarial network (SREDGAN), which minimizes the edge differences between the generated image and its corresponding ground truth. Experimental results on NWPU-VHR-10 and UCAS-AOD datasets demonstrate that our method improves 1.92 and 0.045 in PSNR and SSIM compared with SRGAN, respectively.

scholarly journals Wave-Tracking in the Surf Zone Using Coastal Video Imagery with Deep Neural Networks

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 304 ◽  
Author(s):  
Jinah Kim ◽  
Jaeil Kim ◽  
Taekyung Kim ◽  
Dong Huh ◽  
Sofia Caires
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Surf Zone ◽  
Background Information ◽  
Wave Crest ◽  
Video Enhancement ◽  
Generative Adversarial Network ◽  
Video Imagery ◽  
Adversarial Network ◽  
Spatio Temporal

In this paper, we propose a series of procedures for coastal wave-tracking using coastal video imagery with deep neural networks. It consists of three stages: video enhancement, hydrodynamic scene separation and wave-tracking. First, a generative adversarial network, trained using paired raindrop and clean videos, is applied to remove image distortions by raindrops and to restore background information of coastal waves. Next, a hydrodynamic scene of propagated wave information is separated from surrounding environmental information in the enhanced coastal video imagery using a deep autoencoder network. Finally, propagating waves are tracked by registering consecutive images in the quality-enhanced and scene-separated coastal video imagery using a spatial transformer network. The instantaneous wave speed of each individual wave crest and breaker in the video domain is successfully estimated through learning the behavior of transformed and propagated waves in the surf zone using deep neural networks. Since it enables the acquisition of spatio-temporal information of the surf zone though the characterization of wave breakers inclusively wave run-up, we expect that the proposed framework with the deep neural networks leads to improve understanding of nearshore wave dynamics.

scholarly journals Devnagari Handwritten Characters Image Super-Resolution based on Enhanced SRGAN

2021 ◽  
Vol 16 (1) ◽  
pp. 103-109
Author(s):  
Prasiddha Siwakoti ◽  
Sharad Kumar Ghimire
Keyword(s):  
Super Resolution ◽  
Image Data ◽  
Ground Truth ◽  
Frequency Component ◽  
High Frequency Component ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Batch Normalization ◽  
Statistical Similarity ◽  
Image Super Resolution

The difficulty in machine learning-based image super-resolution is to generate high-frequency component in an image without introducing any artifacts. In this paper, Devnagari handwritten characters image using a generative adversarial network with a classifier is generated in high-resolution which is also classifiable. The generator architecture is modified by removing all batch normalization layers in generator architecture with a residual in residual dense block. Batch normalization is removed because it produces unwanted artifacts in the generated images. A Devnagari handwritten characters classifier is built using CNN. The classifier is used in the network to calculate the content loss. The adversarial loss is obtained from the GAN architecture and both of the losses are added to obtain total loss. Generated HR images is validated using six different evaluation metrics among which MSE, PSNR determines pixel-wise difference and SSIM compares images perceptually. Similarly, FID is used to measure the statistical similarity between the batch of generated images and its original batch. Finally, the Gradient similarity is used to assess the quality of the generated image. From the experimental results, we obtain MSE, PSNR and SSIM as 0.0507, 12.95(dB) and 0.8172 respectively. Similarly, the FID value obtained was 27.5 with the classification accuracy of image data of 98%. The gradient similarity between the generated image and the ground truth obtained was 0.9124.

scholarly journals Cryptospace Invertible Steganography with Conditional Generative Adversarial Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ching-Chun Chang
Keyword(s):  
Neural Networks ◽  
Intelligent Systems ◽  
Deep Neural Networks ◽  
Binary Classification ◽  
Classification Problem ◽  
Generative Adversarial Networks ◽  
Discrimination Function ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Bit Planes

Deep neural networks have become the foundation of many modern intelligent systems. Recently, the author has explored adversarial learning for invertible steganography (ALIS) and demonstrated the potential of deep neural networks to reinvigorate an obsolete invertible steganographic method. With the worldwide popularisation of the Internet of things and cloud computing, invertible steganography can be recognised as a favourable way of facilitating data management and authentication due to the ability to embed information without causing permanent distortion. In light of growing concerns over cybersecurity, it is important to take a step forwards to investigate invertible steganography for encrypted data. Indeed, the multidisciplinary research in invertible steganography and cryptospace computing has received considerable attention. In this paper, we extend previous work and address the problem of cryptospace invertible steganography with deep neural networks. Specifically, we revisit a seminal work on cryptospace invertible steganography in which the problem of message decoding and image recovery is viewed as a type of binary classification. We formulate a general expression encompassing spatial, spectral, and structural analyses towards this particular classification problem and propose a novel discrimination function based on a recurrent conditional generative adversarial network (RCGAN) which predicts bit-planes with stacked neural networks in a top-down manner. Experimental results evaluate the performance of various discrimination functions and validate the superiority of neural-network-aided discrimination function in terms of classification accuracy.

scholarly journals Multiterminal Pathfinding in Practical VLSI Systems with Deep Neural Networks

2022 ◽  
Author(s):  
Dmitry Utyamishev ◽  
Inna Partin-Vaisband
Keyword(s):  
Neural Networks ◽  
High Throughput ◽  
Deep Neural Networks ◽  
Task Complexity ◽  
State Of The Art ◽  
Synthetic Dataset ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Deep Image ◽  
Magnitude Improvement

Abstract A multiterminal obstacle-avoiding pathfinding approach is proposed. The approach is inspired by deep image learning. The key idea is based on training a conditional generative adversarial network (cGAN) to interpret a pathfinding task as a graphical bitmap and consequently map a pathfinding task onto a pathfinding solution represented by another bitmap. To enable the proposed cGAN pathfinding, a methodology for generating synthetic dataset is also proposed. The cGAN model is implemented in Python/Keras, trained on synthetically generated data, evaluated on practical VLSI benchmarks, and compared with state-of-the-art. Due to effective parallelization on GPU hardware, the proposed approach yields a state-of-the-art like wirelength and a better runtime and throughput for moderately complex pathfinding tasks. However, the runtime and throughput with the proposed approach remain constant with an increasing task complexity, promising orders of magnitude improvement over state-of-the-art in complex pathfinding tasks. The cGAN pathfinder can be exploited in numerous high throughput applications, such as, navigation, tracking, and routing in complex VLSI systems. The last is of particular interest to this work.

scholarly journals Fighting Deepfakes by Detecting GAN DCT Anomalies

2021 ◽  
Vol 7 (8) ◽  
pp. 128
Author(s):  
Oliver Giudice ◽  
Luca Guarnera ◽  
Sebastiano Battiato
Keyword(s):  
Neural Networks ◽  
Discrete Cosine Transform ◽  
Deep Neural Networks ◽  
Ad Hoc ◽  
State Of The Art ◽  
Private Life ◽  
Multimedia Content ◽  
Generative Adversarial Network ◽  
Detection Algorithms ◽  
Adversarial Network

To properly contrast the Deepfake phenomenon the need to design new Deepfake detection algorithms arises; the misuse of this formidable A.I. technology brings serious consequences in the private life of every involved person. State-of-the-art proliferates with solutions using deep neural networks to detect a fake multimedia content but unfortunately these algorithms appear to be neither generalizable nor explainable. However, traces left by Generative Adversarial Network (GAN) engines during the creation of the Deepfakes can be detected by analyzing ad-hoc frequencies. For this reason, in this paper we propose a new pipeline able to detect the so-called GAN Specific Frequencies (GSF) representing a unique fingerprint of the different generative architectures. By employing Discrete Cosine Transform (DCT), anomalous frequencies were detected. The β statistics inferred by the AC coefficients distribution have been the key to recognize GAN-engine generated data. Robustness tests were also carried out in order to demonstrate the effectiveness of the technique using different attacks on images such as JPEG Compression, mirroring, rotation, scaling, addition of random sized rectangles. Experiments demonstrated that the method is innovative, exceeds the state of the art and also give many insights in terms of explainability.

Role of General Adversarial Networks in Mammogram Analysis: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 863-877
Author(s):  
Annapoorani Gopal ◽  
Lathaselvi Gandhimaruthian ◽  
Javid Ali
Keyword(s):  
Breast Tumor ◽  
Deep Neural Networks ◽  
Training Data ◽  
Learning Technology ◽  
Breast Cancers ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Tumor Extraction

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.

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