Attaining Real-Time Super-Resolution for Microscopic Images Using GAN

TL;DR

This paper enhances a GAN-based super-resolution method for microscopic images, achieving real-time performance on standard GPUs by optimizing architecture and processing strategies, enabling broader applications in microscopy.

Contribution

It introduces a tiling strategy and architectural modifications to SRGAN, enabling real-time super-resolution microscopy on standard GPU hardware.

Findings

Achieved real-time super-resolution with optimized GPU processing.

Improved image quality and processing speed over existing methods.

Demonstrated applicability across various microscopy domains.

Abstract

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.