scholarly journals Lower Resolution Face Image Higher Resolution Image using Face Conditional Generative Adversarial Network

2021 ◽  
Vol 9 (VI) ◽  
pp. 2866-2872
Author(s):  
Abhinav Chaubey
Keyword(s):  
Visual Quality ◽  
Face Image ◽  
Cutting Edge ◽  
Generative Adversarial Network ◽  
Resolution Image ◽  
Adversarial Network ◽  
Single Face ◽  
Class Models

We propose a novel single face picture super-goal strategy, which is named Face Restrictive Generative Ill-disposed Organization (FCGAN), in view of limit harmony generative antagonistic organizations. To improve the combination speed and reinforce include spread, skip layer association is additionally utilized in the generative and discriminative organizations. Broad tests exhibit that our model accomplishes cutthroat execution contrasted and best in class models To improve the assembly speed and reinforce highlight proliferation, the Generator and Discriminator networks are planned with a skip-association engineering, and both utilizing an auto-encoder structure. Quantitative examinations show that our model accomplishes serious execution contrasted and the cutting edge models dependent on both visual quality and quantitative standards. We accept this excellent face picture created strategy can affect numerous applications in face ID and clever screen

scholarly journals Low Resolution Face Recognition Using Generative Adversarial Network (GAN)

2020 ◽  
Author(s):  
Howard Martin ◽  
Suharjito
Keyword(s):  
Face Recognition ◽  
Super Resolution ◽  
Image Data ◽  
Face Image ◽  
Image Resolution ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Resolution Image ◽  
Surveillance Camera ◽  
Adversarial Network

Abstract Face recognition has a lot of use on smartphone authentication, finding people, etc. Nowadays, face recognition with a constrained environment has achieved very good performance on accuracy. However, the accuracy of existing face recognition methods will gradually decrease when using a dataset with an unconstrained environment. Face image with an unconstrained environment is usually taken from a surveillance camera. In general, surveillance cameras will be placed on the corner of a room or even on the street. So, the image resolution will be low. Low-resolution image will cause the face very hard to be recognized and the accuracy will eventually decrease. That is the main reason why increasing the accuracy of the Low-Resolution Face Recognition (LRFR) problem is still challenging. This research aimed to solve the Low-Resolution Face Recognition (LRFR) problem. The datasets are YouTube Faces Database (YTF) and Labelled Faces in The Wild (LFW). In this research, face image resolution would be decreased using bicubic linear and became the low-resolution image data. Then super resolution methods as the preprocessing step would increase the image resolution. Super resolution methods used in this research are Super resolution GAN (SRGAN) [1] and Enhanced Super resolution GAN (ESRGAN) [2]. These methods would be compared to reach a better accuracy on solving LRFR problem. After increased the image resolution, the image would be recognized using FaceNet. This research concluded that using super resolution as the preprocessing step for LRFR problem has achieved a higher accuracy compared to [3]. The highest accuracy achieved by using ESRGAN as the preprocessing and FaceNet for face recognition with accuracy of 98.96 % and Validation rate 96.757 %.

scholarly journals Disentangled generative adversarial network for low-dose CT

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wenchao Du ◽  
Hu Chen ◽  
Hongyu Yang ◽  
Yi Zhang
Keyword(s):  
Network Architecture ◽  
Low Dose ◽  
Noise Suppression ◽  
State Of The Art ◽  
Visual Quality ◽  
Ct Images ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Suppression Method

AbstractGenerative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

scholarly journals RoboCoDraw: Robotic Avatar Drawing with GAN-Based Style Transfer and Time-Efficient Path Optimization

2020 ◽  
Vol 34 (06) ◽  
pp. 10402-10409
Author(s):  
Tianying Wang ◽  
Wei Qi Toh ◽  
Hao Zhang ◽  
Xiuchao Sui ◽  
Shaohua Li ◽  
...  
Keyword(s):  
Face Image ◽  
Path Optimization ◽  
Robotic Arm ◽  
Human Face ◽  
Generative Adversarial Network ◽  
Style Transfer ◽  
Adversarial Network ◽  
Face Images ◽  
Drawing System ◽  
Collaborative Robot

Robotic drawing has become increasingly popular as an entertainment and interactive tool. In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system that draws stylized human face sketches interactively in front of human users, by using the Generative Adversarial Network (GAN)-based style transfer and a Random-Key Genetic Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm. A core component in this system is the AvatarGAN proposed by us, which generates a cartoon avatar face image from a real human face. AvatarGAN is trained with unpaired face and avatar images only and can generate avatar images of much better likeness with human face images in comparison with the vanilla CycleGAN. After the avatar image is generated, it is fed to a line extraction algorithm and converted to sketches. An RKGA-based path optimization algorithm is applied to find a time-efficient robotic drawing path to be executed by the robotic arm. We demonstrate the capability of RoboCoDraw on various face images using a lightweight, safe collaborative robot UR5.

scholarly journals Complete Face Recovering: An Approach towards Recognizing a Person by a Single Partial Face Image without the Target Photo in Gallery

2020 ◽  
Author(s):  
Yiu-ming Cheung ◽  
Mengke Li
Keyword(s):  
Promising Result ◽  
Face Image ◽  
Attractive Potential ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Discriminative Feature ◽  
New Variant ◽  
Potential Applications ◽  
Benchmark Datasets

Complete face recovering (CFR) is to recover the complete face image of a given partial face image of a target person whose photo may not be included in the gallery set. The CFR has several attractive potential applications but is challenging. As far as we know, the CFR problem has yet to be explored in the literature. This paper therefore proposes an identity-preserved CFR approach (IP-CFR) to addressing the CFR. First, a denoising auto-encoder based network is applied to acquire the discriminative feature. Then, we propose an identity-preserved loss function to keep the personal identity information. Furthermore, the acquired features are fed into a new variant of the generative adversarial network (GAN) to restore the complete face image. In addition, a two-pathway discriminator is leveraged to enhance the quality of the recovered image. Experimental results on the benchmark datasets show the promising result of the proposed approach.

scholarly journals Complete Face Recovering: An Approach towards Recognizing a Person by a Single Partial Face Image without the Target Photo in Gallery

2020 ◽  
Author(s):  
Yiu-ming Cheung ◽  
Mengke Li
Keyword(s):  
Promising Result ◽  
Face Image ◽  
Attractive Potential ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Discriminative Feature ◽  
New Variant ◽  
Potential Applications ◽  
Benchmark Datasets

Complete face recovering (CFR) is to recover the complete face image of a given partial face image of a target person whose photo may not be included in the gallery set. The CFR has several attractive potential applications but is challenging. As far as we know, the CFR problem has yet to be explored in the literature. This paper therefore proposes an identity-preserved CFR approach (IP-CFR) to addressing the CFR. First, a denoising auto-encoder based network is applied to acquire the discriminative feature. Then, we propose an identity-preserved loss function to keep the personal identity information. Furthermore, the acquired features are fed into a new variant of the generative adversarial network (GAN) to restore the complete face image. In addition, a two-pathway discriminator is leveraged to enhance the quality of the recovered image. Experimental results on the benchmark datasets show the promising result of the proposed approach.

scholarly journals Application of a Modified Generative Adversarial Network in the Superresolution Reconstruction of Ancient Murals

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jianfang Cao ◽  
Zibang Zhang ◽  
Aidi Zhao
Keyword(s):  
High Resolution ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Structural Similarity ◽  
Image Feature ◽  
Generative Adversarial Network ◽  
Resolution Image ◽  
High Resolution Image ◽  
Adversarial Network ◽  
Set Up

Considering the problems of low resolution and rough details in existing mural images, this paper proposes a superresolution reconstruction algorithm for enhancing artistic mural images, thereby optimizing mural images. The algorithm takes a generative adversarial network (GAN) as the framework. First, a convolutional neural network (CNN) is used to extract image feature information, and then, the features are mapped to the high-resolution image space of the same size as the original image. Finally, the reconstructed high-resolution image is output to complete the design of the generative network. Then, a CNN with deep and residual modules is used for image feature extraction to determine whether the output of the generative network is an authentic, high-resolution mural image. In detail, the depth of the network increases, the residual module is introduced, the batch standardization of the network convolution layer is deleted, and the subpixel convolution is used to realize upsampling. Additionally, a combination of multiple loss functions and staged construction of the network model is adopted to further optimize the mural image. A mural dataset is set up by the current team. Compared with several existing image superresolution algorithms, the peak signal-to-noise ratio (PSNR) of the proposed algorithm increases by an average of 1.2–3.3 dB and the structural similarity (SSIM) increases by 0.04 = 0.13; it is also superior to other algorithms in terms of subjective scoring. The proposed method in this study is effective in the superresolution reconstruction of mural images, which contributes to the further optimization of ancient mural images.

DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion

2020 ◽  
Vol 29 ◽  
pp. 4980-4995 ◽  
Author(s):  
Jiayi Ma ◽  
Han Xu ◽  
Junjun Jiang ◽  
Xiaoguang Mei ◽  
Xiao-Ping Zhang
Keyword(s):  
Image Fusion ◽  
Generative Adversarial Network ◽  
Resolution Image ◽  
Adversarial Network

scholarly journals A Reference-Guided Double Pipeline Face Image Completion Network

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1969
Author(s):  
Hongrui Liu ◽  
Shuoshi Li ◽  
Hongquan Wang ◽  
Xinshan Zhu
Keyword(s):  
Signal To Noise Ratio ◽  
Receptive Fields ◽  
Similarity Index ◽  
Face Image ◽  
Reference Image ◽  
Image Completion ◽  
Generative Adversarial Network ◽  
Identity Information ◽  
Adversarial Network ◽  
Reference Images

The existing face image completion approaches cannot be utilized to rationally complete damaged face images where their identity information is completely lost due to being obscured by center masks. Hence, in this paper, a reference-guided double-pipeline face image completion network (RG-DP-FICN) is designed within the framework of the generative adversarial network (GAN) completing the identity information of damaged images utilizing reference images with the same identity as damaged images. To reasonably integrate the identity information of reference images into completed images, the reference image is decoupled into identity features (e.g., the contour of eyes, eyebrows, nose) and pose features (e.g., the orientation of face and the positions of the facial features), and then the resulting identity features are fused with posture features of damaged images. Specifically, a lightweight identity predictor is used to extract the pose features; an identity extraction module is designed to compress and globally extract the identity features of the reference images, and an identity transfer module is proposed to effectively fuse identity and pose features by performing identity rendering on different receptive fields. Furthermore, quantitative and qualitative evaluations are conducted on a public dataset CelebA-HQ. Compared to the state-of-the-art methods, the evaluation metrics peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and L1 loss are improved by 2.22 dB, 0.033 and 0.79%, respectively. The results indicate that RG-DP-FICN can generate completed images with reasonable identity, with superior completion effect compared to existing completion approaches.

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