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

• URL: http://arxiv.org/abs/2405.19112v1
• Date: Wed, 29 May 2024 14:20:46 GMT
• Title: Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search
• Authors: Marzieh Gheisari, Auguste Genovesio,
• Abstract summary: Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. Recent methods based on GAN latent search offered a drastic increase in resolution without the need of paired images. 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.
• Score: 2.7194314957925094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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