scholarly journals Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks

2019 ◽  
Author(s):  
Keval Doshi
Keyword(s):  
Generative Adversarial Networks ◽  
Synthetic Image ◽  
Adversarial Networks

scholarly journals Blind Image Separation Method Based on Cascade Generative Adversarial Networks

2021 ◽  
Vol 11 (20) ◽  
pp. 9416
Author(s):  
Fei Jia ◽  
Jindong Xu ◽  
Xiao Sun ◽  
Yongli Ma ◽  
Mengying Ni
Keyword(s):  
Prior Knowledge ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Synthetic Image ◽  
Adversarial Networks ◽  
Training Samples ◽  
Image Separation ◽  
Blind Image ◽  
Insufficient Number ◽  
Image Datasets

To solve the challenge of single-channel blind image separation (BIS) caused by unknown prior knowledge during the separation process, we propose a BIS method based on cascaded generative adversarial networks (GANs). To ensure that the proposed method can perform well in different scenarios and to address the problem of an insufficient number of training samples, a synthetic network is added to the separation network. This method is composed of two GANs: a U-shaped GAN (UGAN), which is used to learn image synthesis, and a pixel-to-attention GAN (PAGAN), which is used to learn image separation. The two networks jointly complete the task of image separation. UGAN uses the unpaired mixed image and the unmixed image to learn the mixing style, thereby generating an image with the “true” mixing characteristics which addresses the problem of an insufficient number of training samples for the PAGAN. A self-attention mechanism is added to the PAGAN to quickly extract important features from the image data. The experimental results show that the proposed method achieves good results on both synthetic image datasets and real remote sensing image datasets. Moreover, it can be used for image separation in different scenarios which lack prior knowledge and training samples.

scholarly journals Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement

2019 ◽  
Vol 7 (7) ◽  
pp. 200 ◽  
Author(s):  
Jaihyun Park ◽  
David K. Han ◽  
Hanseok Ko
Keyword(s):  
Image Enhancement ◽  
Real World ◽  
Input Image ◽  
Generative Adversarial Networks ◽  
Synthetic Image ◽  
Training Procedure ◽  
Weighting Method ◽  
Adversarial Networks ◽  
Underwater Image ◽  
Enhancement Method

In this paper, we propose a novel underwater image enhancement method. Typical deep learning models for underwater image enhancement are trained by paired synthetic dataset. Therefore, these models are mostly effective for synthetic image enhancement but less so for real-world images. In contrast, cycle-consistent generative adversarial networks (CycleGAN) can be trained with unpaired dataset. However, performance of the CycleGAN is highly dependent upon the dataset, thus it may generate unrealistic images with less content information than original images. A novel solution we propose here is by starting with a CycleGAN, we add a pair of discriminators to preserve contents of input image while enhancing the image. As a part of the solution, we introduce an adaptive weighting method for limiting losses of the two types of discriminators to balance their influence and stabilize the training procedure. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-the-art methods on real-world underwater images.

scholarly journals Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation

2021 ◽  
Vol 11 (7) ◽  
pp. 2913
Author(s):  
Christine Dewi ◽  
Rung-Ching Chen ◽  
Yan-Ting Liu ◽  
Hui Yu
Keyword(s):  
Mean Squared Error ◽  
Vision System ◽  
Similarity Index ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Synthetic Image ◽  
Real Image ◽  
Traffic Sign ◽  
Research Issues ◽  
Adversarial Networks

A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from standard models are commonly used to build computer recognition algorithms for acquiring more knowledge on various and low-cost research issues. Convolutional Neural Network (CNN) achieves excellent detection and recognition of traffic signs with sufficient annotated training data. The consistency of the entire vision system is dependent on neural networks. However, locating traffic sign datasets from most countries in the world is complicated. This work uses various generative adversarial networks (GAN) models to construct intricate images, such as Least Squares Generative Adversarial Networks (LSGAN), Deep Convolutional Generative Adversarial Networks (DCGAN), and Wasserstein Generative Adversarial Networks (WGAN). This paper also discusses, in particular, the quality of the images produced by various GANs with different parameters. For processing, we use a picture with a specific number and scale. The Structural Similarity Index (SSIM) and Mean Squared Error (MSE) will be used to measure image consistency. Between the generated image and the corresponding real image, the SSIM values will be compared. As a result, the images display a strong similarity to the real image when using more training images. LSGAN outperformed other GAN models in the experiment with maximum SSIM values achieved using 200 images as inputs, 2000 epochs, and size 32 × 32.

scholarly journals A Survey on Generative Adversarial Networks for imbalance problems in computer vision tasks

2020 ◽  
Author(s):  
Vignesh Sampath ◽  
Iñaki Maurtua ◽  
Juan José Aguilar Martín ◽  
Aitor Gutierrez
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Medical Image Analysis ◽  
Image Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Metallic Surface ◽  
Synthetic Image ◽  
Adversarial Networks ◽  
Model Complex

Abstract Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets.In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

scholarly journals A Survey on Generative Adversarial Networks for imbalance problems in computer vision tasks

2020 ◽  
Author(s):  
Vignesh Sampath ◽  
Iñaki Maurtua ◽  
Juan José Aguilar Martín ◽  
Aitor Gutierrez
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Medical Image Analysis ◽  
Image Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Metallic Surface ◽  
Synthetic Image ◽  
Adversarial Networks ◽  
Model Complex

Abstract Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets. In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examine key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and further provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

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