scholarly journals Super-Resolution Generative Adversarial Network Based on the Dual Dimension Attention Mechanism for Biometric Image Super-Resolution

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7817
Author(s):  
Chi-En Huang ◽  
Yung-Hui Li ◽  
Muhammad Saqlain Aslam ◽  
Ching-Chun Chang
Keyword(s):  
Cloud Computing ◽  
Low Cost ◽  
Recognition Rate ◽  
Super Resolution ◽  
Attention Mechanism ◽  
Image Resolution ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Iot Devices ◽  
Intelligent Security

There exist many types of intelligent security sensors in the environment of the Internet of Things (IoT) and cloud computing. Among them, the sensor for biometrics is one of the most important types. Biometric sensors capture the physiological or behavioral features of a person, which can be further processed with cloud computing to verify or identify the user. However, a low-resolution (LR) biometrics image causes the loss of feature details and reduces the recognition rate hugely. Moreover, the lack of resolution negatively affects the performance of image-based biometric technology. From a practical perspective, most of the IoT devices suffer from hardware constraints and the low-cost equipment may not be able to meet various requirements, particularly for image resolution, because it asks for additional storage to store high-resolution (HR) images, and a high bandwidth to transmit the HR image. Therefore, how to achieve high accuracy for the biometric system without using expensive and high-cost image sensors is an interesting and valuable issue in the field of intelligent security sensors. In this paper, we proposed DDA-SRGAN, which is a generative adversarial network (GAN)-based super-resolution (SR) framework using the dual-dimension attention mechanism. The proposed model can be trained to discover the regions of interest (ROI) automatically in the LR images without any given prior knowledge. The experiments were performed on the CASIA-Thousand-v4 and the CelebA datasets. The experimental results show that the proposed method is able to learn the details of features in crucial regions and achieve better performance in most cases.

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 Panchromatic Image Super-Resolution Via Self Attention-Augmented Wasserstein Generative Adversarial Network

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2158
Author(s):  
Juan Du ◽  
Kuanhong Cheng ◽  
Yue Yu ◽  
Dabao Wang ◽  
Huixin Zhou
Keyword(s):  
Large Scale ◽  
Spatial Information ◽  
Super Resolution ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Similarity Function ◽  
Feature Maps ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Adversarial Network

Panchromatic (PAN) images contain abundant spatial information that is useful for earth observation, but always suffer from low-resolution ( LR) due to the sensor limitation and large-scale view field. The current super-resolution (SR) methods based on traditional attention mechanism have shown remarkable advantages but remain imperfect to reconstruct the edge details of SR images. To address this problem, an improved SR model which involves the self-attention augmented Wasserstein generative adversarial network ( SAA-WGAN) is designed to dig out the reference information among multiple features for detail enhancement. We use an encoder-decoder network followed by a fully convolutional network (FCN) as the backbone to extract multi-scale features and reconstruct the High-resolution (HR) results. To exploit the relevance between multi-layer feature maps, we first integrate a convolutional block attention module (CBAM) into each skip-connection of the encoder-decoder subnet, generating weighted maps to enhance both channel-wise and spatial-wise feature representation automatically. Besides, considering that the HR results and LR inputs are highly similar in structure, yet cannot be fully reflected in traditional attention mechanism, we, therefore, designed a self augmented attention (SAA) module, where the attention weights are produced dynamically via a similarity function between hidden features; this design allows the network to flexibly adjust the fraction relevance among multi-layer features and keep the long-range inter information, which is helpful to preserve details. In addition, the pixel-wise loss is combined with perceptual and gradient loss to achieve comprehensive supervision. Experiments on benchmark datasets demonstrate that the proposed method outperforms other SR methods in terms of both objective evaluation and visual effect.

scholarly journals Target Detection Method for Low-Resolution Remote Sensing Image Based on ESRGAN and ReDet

Photonics ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 431
Author(s):  
Yuwu Wang ◽  
Guobing Sun ◽  
Shengwei Guo
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Detection Method ◽  
Recognition Rate ◽  
Super Resolution ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Adversarial Network

With the widespread use of remote sensing images, low-resolution target detection in remote sensing images has become a hot research topic in the field of computer vision. In this paper, we propose a Target Detection on Super-Resolution Reconstruction (TDoSR) method to solve the problem of low target recognition rates in low-resolution remote sensing images under foggy conditions. The TDoSR method uses the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to perform defogging and super-resolution reconstruction of foggy low-resolution remote sensing images. In the target detection part, the Rotation Equivariant Detector (ReDet) algorithm, which has a higher recognition rate at this stage, is used to identify and classify various types of targets. While a large number of experiments have been carried out on the remote sensing image dataset DOTA-v1.5, the results of this paper suggest that the proposed method achieves good results in the target detection of low-resolution foggy remote sensing images. The principal result of this paper demonstrates that the recognition rate of the TDoSR method increases by roughly 20% when compared with low-resolution foggy remote sensing images.

scholarly journals Mask Attention-SRGAN for Mobile Sensing Networks

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5973
Author(s):  
Chi-En Huang ◽  
Ching-Chun Chang ◽  
Yung-Hui Li
Keyword(s):  
Iris Recognition ◽  
Low Cost ◽  
Recognition Rate ◽  
Super Resolution ◽  
Image Sensor ◽  
Image Sensors ◽  
Mobile Sensing ◽  
Generative Adversarial Network ◽  
Minimum Requirement ◽  
Image Super Resolution

Biometrics has been shown to be an effective solution for the identity recognition problem, and iris recognition, as well as face recognition, are accurate biometric modalities, among others. The higher resolution inside the crucial region reveals details of the physiological characteristics which provides discriminative information to achieve extremely high recognition rate. Due to the growing needs for the IoT device in various applications, the image sensor is gradually integrated in the IoT device to decrease the cost, and low-cost image sensors may be preferable than high-cost ones. However, low-cost image sensors may not satisfy the minimum requirement of the resolution, which definitely leads to the decrease of the recognition accuracy. Therefore, how to maintain high accuracy for biometric systems without using expensive high-cost image sensors in mobile sensing networks becomes an interesting and important issue. In this paper, we proposed MA-SRGAN, a single image super-resolution (SISR) algorithm, based on the mask-attention mechanism used in Generative Adversarial Network (GAN). We modified the latest state-of-the-art (nESRGAN+) in the GAN-based SR model by adding an extra part of a discriminator with an additional loss term to force the GAN to pay more attention within the region of interest (ROI). The experiments were performed on the CASIA-Thousand-v4 dataset and the Celeb Attribute dataset. The experimental results show that the proposed method successfully learns the details of features inside the crucial region by enhancing the recognition accuracies after image super-resolution (SR).

scholarly journals Deep learning achieves super-resolution in fluorescence microscopy

2018 ◽  
Author(s):  
Hongda Wang ◽  
Yair Rivenson ◽  
Yiyin Jin ◽  
Zhensong Wei ◽  
Ronald Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Numerical Models ◽  
Numerical Aperture ◽  
Super Resolution ◽  
Image Resolution ◽  
Stimulated Emission ◽  
Wide Field ◽  
Generative Adversarial Network ◽  
Adversarial Network

AbtsractWe present a deep learning-based method for achieving super-resolution in fluorescence microscopy. This data-driven approach does not require any numerical models of the imaging process or the estimation of a point spread function, and is solely based on training a generative adversarial network, which statistically learns to transform low resolution input images into super-resolved ones. Using this method, we super-resolve wide-field images acquired with low numerical aperture objective lenses, matching the resolution that is acquired using high numerical aperture objectives. We also demonstrate that diffraction-limited confocal microscopy images can be transformed by the same framework into super-resolved fluorescence images, matching the image resolution acquired with a stimulated emission depletion (STED) microscope. The deep network rapidly outputs these super-resolution images, without any iterations or parameter search, and even works for types of samples that it was not trained for.

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