Soft thresholding schemes for multiple signal classification algorithm

TL;DR

This paper introduces soft thresholding schemes for the MUSICAL super-resolution microscopy algorithm, reducing sensitivity to parameter choice and improving robustness while maintaining high resolution.

Contribution

The authors propose a generalized framework for indicator function design in MUSICAL, significantly alleviating threshold sensitivity and subjectivity in super-resolution imaging.

Findings

Soft thresholding schemes improve robustness of MUSICAL.

Trade-offs between resolution and contrast are characterized.

Enhanced out-of-focus light rejection is achieved.

Abstract

Multiple signal classification algorithm (MUSICAL) exploits temporal fluctuations in fluorescence intensity to perform super-resolution microscopy by computing the value of a super-resolving indicator function across a fine sample grid. A key step in the algorithm is the separation of the measurements into signal and noise subspaces, based on a single user-specified parameter called the threshold. The resulting image is strongly sensitive to this parameter and the subjectivity arising from multiple practical factors makes it difficult to determine the right rule of selection. We address this issue by proposing soft thresholding schemes derived from a new generalized framework for indicator function design. We show that the new schemes significantly alleviate the subjectivity and sensitivity of hard thresholding while retaining the super-resolution ability. We also evaluate the trade-off between resolution and contrast and the out-of-focus light rejection using the various indicator functions. Through this, we create significant new insights into the use and further optimization of MUSICAL for a wide range of practical scenarios.