Fluorescence microscopy images are inevitably contaminated by background intensity contributions. Fluorescence from out-of-focus planes and scattered light are important sources of slowly varying, low spatial frequency background, whereas background varying from pixel to pixel (high frequency noise) is introduced by the detection system. Here we present a powerful, easy-to-use software, wavelet-based background and noise subtraction (WBNS), which effectively removes both of these components. To assess its performance, we apply WBNS to synthetic images and compare the results quantitatively with the ground truth and with images processed by other background removal algorithms. We further evaluate WBNS on real images taken with a light-sheet microscope and a super-resolution stimulated emission depletion microscope. For both cases, we compare the WBNS algorithm with hardware-based background removal techniques and present a quantitative assessment of the results. WBNS shows an excellent performance in all these applications and significantly enhances the visual appearance of fluorescence images. Moreover, it may serve as a pre-processing step for further quantitative analysis.

中文翻译：

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基于小波的背景和噪声相减的荧光显微镜图像

荧光显微镜图像不可避免地被背景强度影响所污染。离焦平面的荧光和散射光是缓慢变化的低空间频率背景的重要来源，而检测系统会引入像素之间不同的背景（高频噪声）。在这里，我们介绍了功能强大，易于使用的软件，基于小波的背景和噪声减法（WBNS），可有效去除这两个组件。为了评估其性能，我们将WBNS应用于合成图像，并将结果与​​地面真实情况以及与其他背景去除算法处理过的图像进行定量比较。我们进一步评估了用光片显微镜和超分辨率受激发射损耗显微镜拍摄的真实图像上的WBNS。对于这两种情况 我们将WBNS算法与基于硬件的背景去除技术进行了比较，并对结果进行了定量评估。WBNS在所有这些应用程序中均表现出出色的性能，并显着增强了荧光图像的视觉外观。而且，它可以作为进一步定量分析的预处理步骤。