论文标题
使用gan获得微观图像的实时超分辨率
Attaining Real-Time Super-Resolution for Microscopic Images Using GAN
论文作者
论文摘要
在过去的几年中，几种深度学习模型，尤其是生成性的对抗网络已引起了单个图像超分辨率（SISR）任务的广泛关注。这些方法着重于构建端到端框架，该框架从给定的低分辨率（LR）图像中产生高分辨率（SR）图像，以实现最新性能。本文着重于改善现有的基于深度学习的方法，使用标准GPU实时执行超分辨率显微镜。为此，我们首先提出了一种平铺策略，该策略利用了GPU提供的并行性来加快网络培训过程。此外，我们建议对生成器的架构和SRGAN的歧视者进行简单更改。随后，我们比较了模型产生的输出的质量和运行时间，并在低端台式台面甚至移动显微镜等不同区域开放应用程序。最后，我们探讨了受过训练的网络为不同领域生成高分辨率HR输出的可能性。
In the last few years, several deep learning models, especially Generative Adversarial Networks have received a lot of attention for the task of Single Image Super-Resolution (SISR). These methods focus on building an end-to-end framework, which produce a high resolution(SR) image from a given low resolution(LR) image in a single step to achieve state-of-the-art performance. This paper focuses on improving an existing deep-learning based method to perform Super-Resolution Microscopy in real-time using a standard GPU. For this, we first propose a tiling strategy, which takes advantage of parallelism provided by a GPU to speed up the network training process. Further, we suggest simple changes to the architecture of the generator and the discriminator of SRGAN. Subsequently, We compare the quality and the running time for the outputs produced by our model, opening its applications in different areas like low-end benchtop and even mobile microscopy. Finally, we explore the possibility of the trained network to produce High-Resolution HR outputs for different domains.
