Reconstructing interpretable features in computational super-resolution microscopy via regularized latent search

• Published in: Biological imaging (Cambridge, England) Vol. 4; pp. e8 - 20
• Main Authors: Gheisari, Marzieh, Genovesio, Auguste
• Format: Journal Article
• Language: English
• Published: England Cambridge University Press 2024
• Subjects: Computer applications; Deep learning; diagnostic; generative prior; Image processing; Localization; Methods; Microscopy; Realism; Signal to noise ratio; super-resolution; microscopy; diagnostic; generative prior; super-resolution
• ISSN: 2633-903X; 2633-903X
• DOI: 10.1017/S2633903X24000084

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 generative adversarial network (GAN) latent search offered a drastic increase in resolution without the need of paired images. However, they offer limited reconstruction of the high-resolution (HR) image interpretable features. Here, we propose a robust super-resolution (SR) method based on regularized latent search (RLS) that offers an actionable balance between fidelity to the ground truth (GT) and realism of the recovered image given a distribution prior. The latter allows to split the analysis of a low-resolution (LR) image into a computational SR task performed by deep learning followed by a quantification task performed by a handcrafted algorithm 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 HR details of a specific sample but rather to obtain HR images that preserve explainable and quantifiable differences between conditions.