Light-sheet fluorescence microscopy (LSFM), a planar illumination technique that enables high-resolution imaging of samples, experiences defocused image quality caused by light scattering when photons propagate through thick tissues. To circumvent this issue, dualview imaging is helpful. It allows various sections of the specimen to be scanned ideally by viewing the sample from opposing orientations. Recent image fusion approaches can then be applied to determine in-focus pixels by comparing image qualities of two views locally and thus yield spatially inconsistent focus measures due to their limited field-of-view. Here, we propose BigFUSE, a global context-aware image fuser that stabilizes image fusion in LSFM by considering the global impact of photon propagation in the specimen while determining focus-defocus based on local image qualities. Inspired by the image formation prior in dual-view LSFM, image fusion is considered as estimating a focus-defocus boundary using Bayes Theorem, where (i) the effect of light scattering onto focus measures is included within Likelihood; and (ii) the spatial consistency regarding focus-defocus is imposed in Prior. The expectation-maximum algorithm is then adopted to estimate the focus-defocus boundary. Competitive experimental results show that BigFUSE is the first dual-view LSFM fuser that is able to exclude structured artifacts when fusing information, highlighting its abilities of automatic image fusion.

翻译：光片荧光显微镜（LSFM）是一种能对样本进行高分辨率成像的平面照明显微技术，但当光子穿过厚组织时，会因光散射而导致图像离焦。为克服这一问题，双视图成像十分有效——通过从相反方向观察样本，可理想地扫描标本的不同区域。现有的图像融合方法通过局部比较两视图的图像质量来确定聚焦像素，但由于视野有限，会产生空间不一致的聚焦度量。本文提出BigFUSE，一种全局上下文感知的图像融合器，它在基于局部图像质量判断聚焦-离焦的同时，考虑光子传播对标本的全局影响，从而稳定LSFM中的图像融合。受双视图LSFM中图像形成先验的启发，我们将图像融合视为利用贝叶斯定理估计聚焦-离焦边界：(i) 光散射对聚焦度量的影响被纳入似然函数；(ii) 聚焦-离焦的空间一致性被施加于先验分布。随后采用期望最大化算法估计聚焦-离焦边界。竞争性实验结果表明，BigFUSE是首个能够在信息融合中排除结构伪影的双视图LSFM融合器，凸显了其自动图像融合的能力。