Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, China.
Hefei National Laboratory of Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, China.
J Microsc. 2019 Jul;275(1):24-35. doi: 10.1111/jmi.12799. Epub 2019 May 7.
The quality and information content of biological images can be significantly enhanced by postacquisition processing using deconvolution and denoising. However, when imaging complex biological samples, such as neurons, stained with fluorescence labels, the signal level of different structures can differ by several orders of magnitude. This poses a challenge as current image reconstruction algorithms are focused on recovering low signals and generally have sample-dependent performance, requiring tedious manual tuning. This is one of the main hurdles for their wide adoption by nonspecialists. In this work, we modify the general constrained reconstruction method (in our case utilizing a total variation constraint) so that both bright and dim structures can drive the deconvolution with equal force. In this way, we can simultaneously obtain high-quality reconstruction across a wide range of signals within a single image or image sequence. The algorithm is tested on both simulated and experimental data. When compared with current state-of-art algorithms, our algorithm outperforms others in terms of maintaining the resolution in the high-signal areas and reducing artefacts in the low-signal areas. The algorithm was also tested on image sequences where one set of parameters are used to reconstruct all images, with blind evaluation by a group of biologists demonstrating a marked preference for the images produced by our method. This means that our method is suitable for batch processing of image sequences obtained from either spatial or temporal scanning. LAY DESCRIPTION: Fluorescence microscopy images of complex biological samples contain a wide range of signal levels. This signal variation leads current reconstruction algorithms, which aim to enhance the quality of the raw images, to have sample-dependent performance. In this work, we design a new optimization that allows the reconstruction to "pay equal eqattention to" both bright and dim structures. In this way, we can simultaneously recover both bright and dim structures within a single image or image sequence, as validated when the algorithm was quantitatively tested on both simulated and experimental data. When our method was evaluated alongside current state of art algorithms by a group of biologists, our algorithm was considered qualitatively superior.
通过使用反卷积和去噪进行后期处理，可以显著提高生物图像的质量和信息含量。然而，当对用荧光标记染色的复杂生物样本（如神经元）进行成像时，不同结构的信号水平可能相差几个数量级。这是一个挑战，因为当前的图像重建算法专注于恢复低信号，并且通常具有依赖于样本的性能，需要繁琐的手动调整。这是它们被非专业人士广泛采用的主要障碍之一。在这项工作中，我们修改了通用约束重建方法（在我们的案例中利用总变分约束），以便明亮和暗淡的结构都可以以相等的力度驱动反卷积。通过这种方式，我们可以在单个图像或图像序列内同时获得广泛信号范围内的高质量重建。该算法在模拟和实验数据上进行了测试。与当前最先进的算法相比，我们的算法在保持高信号区域的分辨率和减少低信号区域的伪影方面表现更好。该算法还在图像序列上进行了测试，其中一组参数用于重建所有图像，一组生物学家进行盲评估，他们明显更喜欢我们方法生成的图像。这意味着我们的方法适用于从空间或时间扫描获得的图像序列的批量处理。
复杂生物样本的荧光显微镜图像包含广泛的信号水平。这种信号变化导致当前的重建算法（旨在增强原始图像的质量）具有依赖于样本的性能。在这项工作中，我们设计了一种新的优化方法，允许重建“平等关注”明亮和暗淡的结构。通过这种方式，我们可以在单个图像或图像序列中同时恢复明亮和暗淡的结构，这在对模拟和实验数据进行定量测试时得到了验证。当我们的方法由一组生物学家与当前最先进的算法进行评估时，我们的算法被认为在质量上更优。
