Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
Scientific-Technical Central Units, Instituto de Salud Carlos III (ISCIII), Majadahonda, Spain.
Sci Adv. 2023 Sep;9(35):eadg9245. doi: 10.1126/sciadv.adg9245. Epub 2023 Aug 30.
Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary metal-oxide semiconductor, or photomultiplier tube). MIRO improves current imaging capabilities, enabling fast, low-light optical microscopy, accurate image analysis, and robust machine intelligence when integrated with deep neural networks. This expands the range of biological knowledge that can be obtained from fluorescence microscopy.
荧光显微镜是生物学研究中不可或缺且极富信息量的工具之一，但可观察到的生物学现象的程度本质上取决于所获取图像的内容和质量。为了解决可能降低这些图像质量的不同噪声源问题，我们引入了一种通过最优稀疏表示进行多尺度图像恢复的算法（MIRO）。MIRO 是一种确定性框架，它对采集过程进行建模，并使用逐像素噪声校正来改善图像质量。我们的研究表明，无论使用何种探测器（例如电子倍增电荷耦合器件、科学互补金属氧化物半导体或光电倍增管），该方法都可以显著恢复荧光信号。MIRO 提高了当前的成像能力，在与深度神经网络集成时，可实现快速、低光的光学显微镜、准确的图像分析和稳健的机器智能。这扩展了可以从荧光显微镜获得的生物学知识范围。
