Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search

TL;DR

This paper introduces a regularized latent search method for super-resolution microscopy that balances image fidelity and interpretability, enabling quantifiable biological feature analysis without extensive paired training data.

Contribution

The proposed RLS method combines deep learning and handcrafted algorithms to produce interpretable super-resolution images with improved fidelity and biological relevance.

Findings

Balances fidelity and realism in super-resolution images

Enables quantification of biological features from low-res images

Applicable for diagnostics on mobile devices

Abstract

Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such methods often require a large set of hard-to-get low-res/high-res image pairs and produce synthetic images with a moderate increase in resolution. Conversely, recent methods based on GAN latent search offered a drastic increase in resolution without the need of paired images. However, they offer limited reconstruction of the high-resolution image interpretable features. Here, we propose a robust super-resolution method based on regularized latent search~(RLS) that offers an actionable balance between fidelity to the ground-truth and realism of the recovered image given a distribution prior. The latter allows to split the analysis of a low-resolution image into a computational super-resolution task performed by deep learning followed by a quantification task performed by a handcrafted algorithm and based on interpretable biological features. This two-step process holds potential for various applications such as diagnostics on mobile devices, where the main aim is not to recover the high-resolution details of a specific sample but rather to obtain high-resolution images that preserve explainable and quantifiable differences between conditions.