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

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.

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.

Gastronomic Algorithms: Artistic and Sensory Exploration of Alain Passard's Michelin Plates in the Manner of Giuseppe Arcimboldo with GANs

Leonardo ◽  
2021 ◽  
pp. 1-11
Author(s):  
Emily L. Spratt
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Artistic Collaboration ◽  
Recent Advances ◽  
Learning Techniques ◽  
Data Scientist ◽  
The Senses ◽  
Computer Programmer

Abstract Although recent advances in artificial intelligence to generate images with deep learning techniques, especially generative adversarial networks (GANs), have offered radically new opportunities for its creative applications, there has been little investigation into its use as a tool to explore the senses beyond vision alone. In an artistic collaboration that brought Chef Alain Passard, art historian and data scientist Emily Spratt, and computer programmer Thomas Fan together, photographs of the three-star Michelin plates from the Parisian restaurant Arpège were used as a springboard to explore the art of culinary presentation in the manner of the Renaissance painter Giuseppe Arcimboldo.

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 Recent Advances of Image Steganography With Generative Adversarial Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 60575-60597
Author(s):  
Jia Liu ◽  
Yan Ke ◽  
Zhuo Zhang ◽  
Yu Lei ◽  
Jun Li ◽  
...  
Keyword(s):  
Image Steganography ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Recent Advances
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