Quantitatively mapping local quality at super-resolution scale by rolling Fourier ring correlation

Abstract In fluorescence microscopy, computational algorithms have been developed to suppress noise, enhance contrast, and even enable super-resolution (SR). The local quality of the images may vary on multiple scales and these differences can lead to misconceptions. However, current mapping methods fail to finely estimate the local quality, challenging to associate the SR scale content. Here, we develop a rolling Fourier ring correlation (rFRC) to quantify reconstruction quality down to the SR scale. To visually pinpoint regions with low reliability, we combine a filtered rFRC with a modified resolution scaled error map (RSM), providing a comprehensive and concise map for further examination. We demonstrate their performances on different SR imaging modalities, and the resulting quantitative maps enable better SR images integrated from different reconstructions. Beyond that, as a model-independent assessment, we show its applications in evaluating the local restoration qualities for the deep-learning methods.