The contrast and resolution of images obtained with optical microscopes can be improved by deconvolution and computational fusion of multiple views of the same sample, but these methods are computationally expensive for large datasets. Here we describe theoretical and practical advances in algorithm and software design that result in image processing times that are tenfold to several thousand fold faster than with previous methods. First, we show that an ‘unmatched back projector’ accelerates deconvolution relative to the classic Richardson–Lucy algorithm by at least tenfold. Second, three-dimensional image-based registration with a graphics processing unit enhances processing speed 10- to 100-fold over CPU processing. Third, deep learning can provide further acceleration, particularly for deconvolution with spatially varying point spread functions. We illustrate our methods from the subcellular to millimeter spatial scale on diverse samples, including single cells, embryos and cleared tissue. Finally, we show performance enhancement on recently developed microscopes that have improved spatial resolution, including dual-view cleared-tissue light-sheet microscopes and reflective lattice light-sheet microscopes.

使用光学显微镜获得的图像的对比度和分辨率可以通过对同一样本的多个视图进行反卷积和计算融合来提高，但这些方法对于大型数据集的计算成本很高。在这里，我们描述了算法和软件设计的理论和实践进步，这些进步导致图像处理时间比以前的方法快十倍到几千倍。首先，我们展示了“无与伦比的背投”相对于经典的 Richardson-Lucy 算法将反卷积加速了至少十倍。其次，使用图形处理单元进行基于三维图像的配准可将处理速度提高 10 到 100 倍于 CPU 处理。第三，深度学习可以提供进一步的加速，特别是对于具有空间变化的点扩散函数的反卷积。我们在不同样本（包括单细胞、胚胎和清除组织）上说明了从亚细胞到毫米空间尺度的方法。最后，我们展示了最近开发的提高空间分辨率的显微镜的性能增强，包括双视图透明组织光片显微镜和反射晶格光片显微镜。