Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopy

TL;DR

Self-STORM introduces a self-supervised deep unrolled autoencoder for super-resolution microscopy that outperforms supervised methods and does not require labeled training data, enabling robust, dynamic imaging of live cells.

Contribution

It presents a novel self-supervised, model-based autoencoder approach for super-resolution microscopy that improves robustness and generalization without external training data.

Findings

Outperforms supervised deep learning methods in super-resolution tasks.

Enables dynamic, diffraction-limited imaging with no labeled data.

Enhances generalization in sparse recovery frameworks.

Abstract

The use of fluorescent molecules to create long sequences of low-density, diffraction-limited images enables highly-precise molecule localization. However, this methodology requires lengthy imaging times, which limits the ability to view dynamic interactions of live cells on short time scales. Many techniques have been developed to reduce the number of frames needed for localization, from classic iterative optimization to deep neural networks. Particularly, deep algorithm unrolling utilizes both the structure of iterative sparse recovery algorithms and the performance gains of supervised deep learning. However, the robustness of this approach is highly dependant on having sufficient training data. In this paper we introduce deep unrolled self-supervised learning, which alleviates the need for such data by training a sequence-specific, model-based autoencoder that learns only from given measurements. Our proposed method exceeds the performance of its supervised counterparts, thus allowing for robust, dynamic imaging well below the diffraction limit without any labeled training samples. Furthermore, the suggested model-based autoencoder scheme can be utilized to enhance generalization in any sparse recovery framework, without the need for external training data.