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Welcome to the PANELJ wiki!

This repository is distributed as accompanying software for publication: Weisong Zhao et al. Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation, Light: Science & Applications (2023), allowing rFRC mapping and simplified PANEL pinpointing.

Uncertainty types: There are exiting two major categories of reconstruction uncertainty in computational microscopy imaging, including the model uncertainty and the data uncertainty. The model uncertainty are primarily caused by the difference between the artificially created estimation model and its physical, real-world counterpart, which can be detected and minimized by careful calibration of the optical microscopy system or enough training data in learning-based applications. The data uncertainty are mostly introduced by joint effects of the noise condition and sampling capability of the hardware equipment. Notably, different from the model uncertainty, the data uncertainty are free from the model, inevitable, and may be hard to be suppressed by system calibration or adding more training datasets.

rFRC: The rFRC is for quantitatively mapping the local image quality (effective resolution, data uncertainty). The lower effective resolution gives a higher probability to the error existence, and thus we can use it to represent the uncertainty revealing the error distribution.

PANEL: In this plugin, PANEL is a filtered rFRC map, for biologists to qualitatively pinpoint regions with low reliability as a concise visualization

Note: Our rFRC and PANEL using two independent captures cannot fully pinpoint the unreliable regions induced by the model bias, which would require more extensive characterization and correction routines based on the underlying theory of the corresponding models.