In fluorescence microscopy, computational algorithms have been developed to suppress noise, enhance contrast, and even enable super-resolution (SR). However, the local quality of the images may vary on multiple scales, and these differences can lead to misconceptions. 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) method to evaluate the reconstruction uncertainties down to SR scale. To visually pinpoint regions with low reliability, a filtered rFRC is combined with a modified resolution-scaled error map (RSM), offering a comprehensive and concise map for further examination. We demonstrate their performances on various SR imaging modalities, and the resulting quantitative maps enable better SR images integrated from different reconstructions. Overall, we expect that our framework can become a routinely used tool for biologists in assessing their image datasets in general and inspire further advances in the rapidly developing field of computational imaging.

在荧光显微镜中，已经开发出计算算法来抑制噪声、增强对比度，甚至实现超分辨率 (SR)。然而，图像的局部质量可能在多个尺度上有所不同，这些差异可能会导致误解。当前的绘图方法无法精细估计局部质量，因此很难关联 SR 尺度内容。在这里，我们开发了一种滚动傅里叶环相关（rFRC）方法来评估低至 SR 尺度的重建不确定性。为了直观地查明可靠性较低的区域，将过滤后的 rFRC 与修改后的分辨率缩放误差图 (RSM) 相结合，为进一步检查提供全面而简洁的图。我们展示了它们在各种 SR 成像模式上的性能，并且生成的定量图能够从不同的重建中集成更好的 SR 图像。总的来说，我们期望我们的框架能够成为生物学家评估其图像数据集的常规工具，并激发快速发展的计算成像领域的进一步进步。