Optimal Physical Preprocessing for Example-Based Super-Resolution

TL;DR

This paper introduces a method to optimize the physical illumination patterns in Fourier ptychographic microscopy, enhancing high-resolution image reconstruction by jointly optimizing hardware and software components using deep learning.

Contribution

It presents a novel joint optimization approach for both illumination hardware and reconstruction algorithms, improving super-resolution microscopy outcomes.

Findings

Joint optimization improves image quality over software-only methods

Simulated data confirms enhanced resolution and field-of-view

Method enables single-shot high-resolution imaging

Abstract

In example-based super-resolution, the function relating low-resolution images to their high-resolution counterparts is learned from a given dataset. This data-driven approach to solving the inverse problem of increasing image resolution has been implemented with deep learning algorithms. In this work, we explore modifying the imaging hardware in order to collect more informative low-resolution images for better ultimate high-resolution image reconstruction. We show that this "physical preprocessing" allows for improved image reconstruction with deep learning in Fourier ptychographic microscopy. Fourier ptychographic microscopy is a technique allowing for both high resolution and high field-of-view at the cost of temporal resolution. In Fourier ptychographic microscopy, variable illumination patterns are used to collect multiple low-resolution images. These low-resolution images are then computationally combined to create an image with resolution exceeding that of any single image from the microscope. We use deep learning to jointly optimize the illumination pattern with the post-processing reconstruction algorithm for a given sample type, allowing for single-shot imaging with both high resolution and high field-of-view. We demonstrate, with simulated data, that the joint optimization yields improved image reconstruction as compared with sole optimization of the post-processing reconstruction algorithm.