Scientific Image Restoration Anywhere

TL;DR

This paper evaluates the performance of a scientific image restoration model, TomoGAN, on edge computing devices like Google Edge TPU and NVIDIA Jetson, demonstrating that they can deliver fast, accurate denoising suitable for real-time scientific applications.

Contribution

The study adapts TomoGAN for edge devices, assesses its performance, and proposes methods to mitigate accuracy loss from quantization, enabling effective low-latency scientific image restoration.

Findings

Edge devices achieve comparable accuracy to CPU/GPU models.

Edge TPU provides 3x faster inference than CPU.

Models denoise 1024x1024 images in less than a second.

Abstract

The use of deep learning models within scientific experimental facilities frequently requires low-latency inference, so that, for example, quality control operations can be performed while data are being collected. Edge computing devices can be useful in this context, as their low cost and compact form factor permit them to be co-located with the experimental apparatus. Can such devices, with their limited resources, can perform neural network feed-forward computations efficiently and effectively? We explore this question by evaluating the performance and accuracy of a scientific image restoration model, for which both model input and output are images, on edge computing devices. Specifically, we evaluate deployments of TomoGAN, an image-denoising model based on generative adversarial networks developed for low-dose x-ray imaging, on the Google Edge TPU and NVIDIA Jetson. We adapt TomoGAN for edge execution, evaluate model inference performance, and propose methods to address the accuracy drop caused by model quantization. We show that these edge computing devices can deliver accuracy comparable to that of a full-fledged CPU or GPU model, at speeds that are more than adequate for use in the intended deployments, denoising a 1024 x 1024 image in less than a second. Our experiments also show that the Edge TPU models can provide 3x faster inference response than a CPU-based model and 1.5x faster than an edge GPU-based model. This combination of high speed and low cost permits image restoration anywhere.