A Generative Adversarial Network to Denoise Depth Maps for Quality Improvement of DIBR-Synthesized Stereoscopic Images

2021 ◽  
Author(s):  
Chuang Zhang ◽  
Xian-wen Sun ◽  
Jiawei Xu ◽  
Xiao-yu Huang ◽  
Gui-yue Yu ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Generative Adversarial Network ◽  
Stereoscopic Images ◽  
Depth Maps ◽  
Adversarial Network

VeIGAN: Vectorial Inpainting Generative Adversarial Network for Depth Maps Object Removal

2019 ◽  
Author(s):  
Lucas P. N. Matias ◽  
Marc Sons ◽  
Jefferson R. Souza ◽  
Denis F. Wolf ◽  
Christoph Stiller
Keyword(s):  
Generative Adversarial Network ◽  
Depth Maps ◽  
Adversarial Network ◽  
Object Removal

scholarly journals Depth Map Upsampling via Multi-Modal Generative Adversarial Network

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1587 ◽  
Author(s):  
Daniel Tan ◽  
Jun-Ming Lin ◽  
Yu-Chi Lai ◽  
Joel Ilao ◽  
Kai-Lung Hua
Keyword(s):  
Autonomous Robots ◽  
Smart Cities ◽  
Super Resolution ◽  
Depth Map ◽  
Color Images ◽  
Test Time ◽  
Generative Adversarial Network ◽  
Depth Maps ◽  
Adversarial Network ◽  
Sharp Edges

Autonomous robots for smart homes and smart cities mostly require depth perception in order to interact with their environments. However, depth maps are usually captured in a lower resolution as compared to RGB color images due to the inherent limitations of the sensors. Naively increasing its resolution often leads to loss of sharpness and incorrect estimates, especially in the regions with depth discontinuities or depth boundaries. In this paper, we propose a novel Generative Adversarial Network (GAN)-based framework for depth map super-resolution that is able to preserve the smooth areas, as well as the sharp edges at the boundaries of the depth map. Our proposed model is trained on two different modalities, namely color images and depth maps. However, at test time, our model only requires the depth map in order to produce a higher resolution version. We evaluated our model both quantitatively and qualitatively, and our experiments show that our method performs better than existing state-of-the-art models.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

Role of General Adversarial Networks in Mammogram Analysis: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 863-877
Author(s):  
Annapoorani Gopal ◽  
Lathaselvi Gandhimaruthian ◽  
Javid Ali
Keyword(s):  
Breast Tumor ◽  
Deep Neural Networks ◽  
Training Data ◽  
Learning Technology ◽  
Breast Cancers ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Tumor Extraction

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.

scholarly journals Realistic Sonar Image Simulation Using Generative Adversarial Network

2019 ◽  
Vol 52 (21) ◽  
pp. 291-296 ◽  
Author(s):  
Minsung Sung ◽  
Jason Kim ◽  
Juhwan Kim ◽  
Son-Cheol Yu
Keyword(s):  
Image Simulation ◽  
Generative Adversarial Network ◽  
Sonar Image ◽  
Adversarial Network
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