生成对抗网络在图像数据增强移动应用中的实现

Oleksandr Striuk, Yuriy Kondratenko
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引用次数: 0

摘要

本文旨在探索和研究gan作为移动设备的工具,可以从低分辨率样本中生成高分辨率图像并减少模糊。此外,作者还分析了GAN、SRGAN和ESRGAN损失函数的具体特点及其特征。gan被广泛应用于图像处理的各种应用任务。他们能够合成、组合和恢复高质量的图形样本,几乎与真实数据无法区分。研究的主要范围是研究在上述任务中使用gan的可能性,以及它们在移动应用程序中的潜在实现。
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Implementation of Generative Adversarial Networks in Mobile Applications for Image Data Enhancement
This article aims to explore and research GANs as a tool for mobile devices that can generate high-resolution images from low-resolution samples and reduce blurring. In addition, the authors also analyse the specifics of GAN, SRGAN, and ESRGAN loss functions and their features. GANs are widely used for a vast range of applied tasks for image manipulations. They’re able to synthesize, combine, and restore graphical samples of high quality that are almost indistinguishable from real data. The main scope of the research is to study the possibility to use GANs for the said tasks, and their potential implementation in mobile applications.
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