scholarly journals Performance Evaluation of Generative Adversarial Networks for Computer Vision Applications

2020 ◽  
Vol 25 (1) ◽  
pp. 83-92
Author(s):  
Sravani Nannapaneni ◽  
Venkatramaphanikumar Sistla ◽  
Venkata Krishna Kishore Kolli
Keyword(s):  
Computer Vision ◽  
Performance Evaluation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Computer Vision Applications

scholarly journals Content Fidelity of Deep Learning Methods for Clipping and Over-exposure Correction

2021 ◽  
Vol 2021 (1) ◽  
pp. 43-48
Author(s):  
Mekides Assefa Abebe
Keyword(s):  
Computer Vision ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Generative Adversarial Networks ◽  
Tone Mapping ◽  
Objective Functions ◽  
Related Research ◽  
Adversarial Networks ◽  
Computer Vision Applications ◽  
Research Domains

Exposure problems, due to standard camera sensor limitations, often lead to image quality degradations such as loss of details and change in color appearance. The quality degradations further hiders the performances of imaging and computer vision applications. Therefore, the reconstruction and enhancement of uderand over-exposed images is essential for various applications. Accordingly, an increasing number of conventional and deep learning reconstruction approaches have been introduced in recent years. Most conventional methods follow color imaging pipeline, which strongly emphasize on the reconstructed color and content accuracy. The deep learning (DL) approaches have conversely shown stronger capability on recovering lost details. However, the design of most DL architectures and objective functions don’t take color fidelity into consideration and, hence, the analysis of existing DL methods with respect to color and content fidelity will be pertinent. Accordingly, this work presents performance evaluation and results of recent DL based overexposure reconstruction solutions. For the evaluation, various datasets from related research domains were merged and two generative adversarial networks (GAN) based models were additionally adopted for tone mapping application scenario. Overall results show various limitations, mainly for severely over-exposed contents, and a promising potential for DL approaches, GAN, to reconstruct details and appearance.

scholarly journals Super Resolution: A Simplified Approach Using GANs

2020 ◽  
Vol 5 (7) ◽  
pp. 188-191
Author(s):  
Dr. Naveena C ◽  
Thanush M ◽  
Vinay N.B ◽  
Yaser Ahmed N
Keyword(s):  
Computer Vision ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Digital Cameras ◽  
Simplified Approach ◽  
Resolution Image ◽  
Adversarial Networks ◽  
Current Scenario ◽  
Computer Vision Applications ◽  
The Cost

The project entitled “Super-Resolution: A simplified approach using GANs”, aims to construct a Higher Resolution image from a Lower Resolution image, without losing much detail. In other words, it is a process of up-sampling of an under-sampled image. In the current scenario, there is a high reliance on hardware improvements to capture better highresolution images. Although many digital cameras provide enough HR-imagery, the cost to construct and purchase such a high-end camera is high. Also, many computer vision applications like medical imaging, forensic and many more still have a strong demand for higher resolution imagery which is likely to be exceeded by the capabilities of these HR digital cameras we have today. To cope with this demand, a method to generate an HR image is shown using Generative Adversarial Networks (GANs).

scholarly journals Recent Advances of Generative Adversarial Networks in Computer Vision

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 14985-15006 ◽  
Author(s):  
Yang-Jie Cao ◽  
Li-Li Jia ◽  
Yong-Xia Chen ◽  
Nan Lin ◽  
Cong Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Recent Advances

scholarly journals Guest Editorial: Generative Adversarial Networks for Computer Vision

2020 ◽  
Vol 128 (10-11) ◽  
pp. 2363-2365
Author(s):  
Jun-Yan Zhu ◽  
Hongsheng Li ◽  
Eli Shechtman ◽  
Ming-Yu Liu ◽  
Jan Kautz ◽  
...  
Keyword(s):  
Computer Vision ◽  
Guest Editorial ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

Handwritten Signature Spoofing With Conditional Generative Adversarial Nets

2022 ◽  
pp. 98-110
Author(s):  
Md Fazle Rabby ◽  
Md Abdullah Al Momin ◽  
Xiali Hei
Keyword(s):  
Computer Vision ◽  
Image Synthesis ◽  
Research Topic ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Image Translation ◽  
Handwritten Signature ◽  
Condition Vector

Generative adversarial networks have been a highly focused research topic in computer vision, especially in image synthesis and image-to-image translation. There are a lot of variations in generative nets, and different GANs are suitable for different applications. In this chapter, the authors investigated conditional generative adversarial networks to generate fake images, such as handwritten signatures. The authors demonstrated an implementation of conditional generative adversarial networks, which can generate fake handwritten signatures according to a condition vector tailored by humans.

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 ◽  
Generative Adversarial Networks ◽  
Metallic Surface ◽  
Application Development ◽  
Adversarial Networks ◽  
Surface Defect Detection ◽  
Image Models

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

scholarly journals Rain Removal from images based on Computer Vision and conditional Generative Adversarial Networks

2020 ◽  
Vol 1570 ◽  
pp. 012064
Author(s):  
Yunfei Li ◽  
Lidan Wang ◽  
Taixing Chen ◽  
Ziyuan Wang ◽  
Shukai Duan
Keyword(s):  
Computer Vision ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Rain Removal

scholarly journals Generative Adversarial Networks in Computer Vision

2021 ◽  
Vol 54 (2) ◽  
pp. 1-38
Author(s):  
Zhengwei Wang ◽  
Qi She ◽  
Tomás E. Ward
Keyword(s):  
Computer Vision ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Image Generation ◽  
Adversarial Networks ◽  
Image Translation ◽  
The Status ◽  
Loss Variant ◽  
Future Research Directions ◽  
Application Requirements

Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably their most significant impact has been in the area of computer vision where great advances have been made in challenges such as plausible image generation, image-to-image translation, facial attribute manipulation, and similar domains. Despite the significant successes achieved to date, applying GANs to real-world problems still poses significant challenges, three of which we focus on here. These are as follows: (1) the generation of high quality images, (2) diversity of image generation, and (3) stabilizing training. Focusing on the degree to which popular GAN technologies have made progress against these challenges, we provide a detailed review of the state-of-the-art in GAN-related research in the published scientific literature. We further structure this review through a convenient taxonomy we have adopted based on variations in GAN architectures and loss functions. While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress toward addressing practical challenges relevant to computer vision. Accordingly, we review and critically discuss the most popular architecture-variant, and loss-variant GANs, for tackling these challenges. Our objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress toward critical computer vision application requirements. As we do this we also discuss the most compelling applications in computer vision in which GANs have demonstrated considerable success along with some suggestions for future research directions. Codes related to the GAN-variants studied in this work is summarized on https://github.com/sheqi/GAN_Review.

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