Learning imaging mechanism directly from optical microscopy observations
This provides a feasible scheme for learning hidden imaging mechanisms in optical microscopy, potentially applicable to other systems, though it appears incremental as it builds on existing autoencoder and physics-informed methods.
The paper tackles the challenge of directly extracting the point spread function (PSF) from optical microscopy images without prior knowledge, proposing a self-supervised physics-informed masked autoencoder (PiMAE) that achieves significant accuracy and noise robustness, outperforming DeepSTORM and the Richardson-Lucy algorithm by 19.6% and 50.7% in synthetic tasks.
Optical microscopy image plays an important role in scientific research through the direct visualization of the nanoworld, where the imaging mechanism is described as the convolution of the point spread function (PSF) and emitters. Based on a priori knowledge of the PSF or equivalent PSF, it is possible to achieve more precise exploration of the nanoworld. However, it is an outstanding challenge to directly extract the PSF from microscopy images. Here, with the help of self-supervised learning, we propose a physics-informed masked autoencoder (PiMAE) that enables a learnable estimation of the PSF and emitters directly from the raw microscopy images. We demonstrate our method in synthetic data and real-world experiments with significant accuracy and noise robustness. PiMAE outperforms DeepSTORM and the Richardson-Lucy algorithm in synthetic data tasks with an average improvement of 19.6\% and 50.7\% (35 tasks), respectively, as measured by the normalized root mean square error (NRMSE) metric. This is achieved without prior knowledge of the PSF, in contrast to the supervised approach used by DeepSTORM and the known PSF assumption in the Richardson-Lucy algorithm. Our method, PiMAE, provides a feasible scheme for achieving the hidden imaging mechanism in optical microscopy and has the potential to learn hidden mechanisms in many more systems.