Laboratorio delle Tecnologie per Terapie Avanzate, Università di Ferrara, Ferrara, Italy.
Sci Rep. 2013;3:2523. doi: 10.1038/srep02523.
Although deconvolution can improve the quality of any type of microscope, the high computational time required has so far limited its massive spreading. Here we demonstrate the ability of the scaled-gradient-projection (SGP) method to provide accelerated versions of the most used algorithms in microscopy. To achieve further increases in efficiency, we also consider implementations on graphic processing units (GPUs). We test the proposed algorithms both on synthetic and real data of confocal and STED microscopy. Combining the SGP method with the GPU implementation we achieve a speed-up factor from about a factor 25 to 690 (with respect the conventional algorithm). The excellent results obtained on STED microscopy images demonstrate the synergy between super-resolution techniques and image-deconvolution. Further, the real-time processing allows conserving one of the most important property of STED microscopy, i.e the ability to provide fast sub-diffraction resolution recordings.
虽然去卷积可以提高任何类型显微镜的质量，但所需的高计算时间迄今为止限制了其大规模普及。在这里，我们展示了尺度梯度投影（SGP）方法提供最常用显微镜算法加速版本的能力。为了进一步提高效率，我们还考虑在图形处理单元（GPU）上的实现。我们在共聚焦和 STED 显微镜的合成和真实数据上测试了所提出的算法。通过将 SGP 方法与 GPU 实现相结合，我们实现了大约 25 到 690 倍的加速因子（相对于传统算法）。在 STED 显微镜图像上获得的优异结果证明了超分辨率技术和图像去卷积之间的协同作用。此外，实时处理允许保留 STED 显微镜最重要的特性之一，即提供快速亚衍射分辨率记录的能力。
