Realistic River Image Synthesis using Deep Generative Adversarial Networks

2020-02-14 · Akshat Gautam, Muhammed Sit, Ibrahim Demir

In this paper, we demonstrated a practical application of realistic river image generation using deep learning. Specifically, we explored a generative adversarial network (GAN) model capable of generating high-resolution and realistic river images that can be used to support modeling and analysis in surface water estimation, river meandering, wetland loss, and other hydrological research studies. First, we have created an extensive repository of overhead river images to be used in training. Second, we incorporated the Progressive Growing GAN (PGGAN), a network architecture that iteratively trains smaller-resolution GANs to gradually build up to a very high resolution to generate high quality (i.e., 1024x1024) synthetic river imagery. With simpler GAN architectures, difficulties arose in terms of exponential increase of training time and vanishing/exploding gradient issues, which the PGGAN implementation seemed to significantly reduce. The results presented in this study show great promise in generating high-quality images and capturing the details of river structure and flow to support hydrological research, which often requires extensive imagery for model performance.

📄 PDF Abstract BibTeX arXiv:2003.00826

Code (0)

등록된 구현이 없습니다.

Tasks

Generative Adversarial NetworkImage Generation

Methods 이 논문이 사용한 방법론

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

Dogecoin Customer Service Number +1-833-534-1729 설명 없음

Photorealism in Driving Simulations: Blending Generative Adversarial Image Synthesis with Rendering

2020-07-31 · Ekim Yurtsever, Dongfang Yang, Ibrahim Mert Koc, Keith A. Redmill

Driving simulators play a large role in developing and testing new intelligent vehicle systems. The visual fidelity of the simulation is critical for building vision-based algorithms and conducting human driver experimen…

Generative Adversarial Network Image Generation

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

2017-10-19 · Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang 외

Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Netwo…

Generative Adversarial Network Image Generation Text-to-Image Generation

Generative Adversarial Networks Synthesize Realistic OCT Images of the Retina

2019-02-18 · Stephen G. Odaibo, M. D., M. S.

We report, to our knowledge, the first end-to-end application of Generative Adversarial Networks (GANs) towards the synthesis of Optical Coherence Tomography (OCT) images of the retina. Generative models have gained rece…

Imitating Driver Behavior with Generative Adversarial Networks

2017-01-24 · Alex Kuefler, Jeremy Morton, Tim Wheeler, Mykel Kochenderfer

The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavio…

Imitation Learning

Dual Pyramid Generative Adversarial Networks for Semantic Image Synthesis

2022-10-08 · Shijie Li, Ming-Ming Cheng, Juergen Gall

The goal of semantic image synthesis is to generate photo-realistic images from semantic label maps. It is highly relevant for tasks like content generation and image editing. Current state-of-the-art approaches, however…

Generative Adversarial Network Image Generation Image-to-Image Translation