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首页> 外文期刊>IEEE Transactions on Image Processing >Parameter-Free Gaussian PSF Model for Extended Depth of Field in Brightfield Microscopy
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Parameter-Free Gaussian PSF Model for Extended Depth of Field in Brightfield Microscopy

机译：明亮菲尔德显微镜中延长景深的可参数Gaussian PSF模型

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摘要

Due to their limited depth of field, conventional brightfield microscopes cannot image thick specimens entirely in focus. A common way to obtain an all-in-focus image is to acquire a z-stack of images by optically sectioning the specimen and then apply a multi-focus fusion method. Unfortunately, for undersampled image stacks, fusion methods cannot remove the blur in regions where the in-focus position is between two optical sections. In this work, we propose a parameter-free Gaussian PSF model in which the all-in-focus image together with both the depth map and sampling distances in image plane are estimated from the image sequence automatically, without knowledge on the z-stack acquisition. In a maximum a posteriori framework, an iteratively reweighted least squares method is used to estimate the image and an adaptive scaled gradient descent method is utilized to estimate the depth map and sampling distances efficiently. Experiments on synthetic and real data demonstrate that the proposed method outperforms the current state-of-the-art, mitigating fusion artifacts and recovering sharper edges.
机译：由于它们有限的景深，传统的明菲尔德显微镜不能完全以浓厚的焦点图像图像。获得全焦焦点图像的常见方法是通过光学切断样本来获取Z叠图像，然后应用多聚焦融合方法。遗憾的是，对于向下采样的图像堆叠，融合方法不能在聚焦位置在两个光学部分之间的区域中移除模糊。在这项工作中，我们提出了一种可参与的高斯高斯PSF模型，其中从图像序列估计了一个无焦点图像以及图像平面中的深度图和采样距离，在没有关于Z堆叠采集的知识。在最大后后框架中，使用迭代重新重量的最小二乘法来估计图像，并且利用自适应缩放梯度下降方法来有效地估计深度图和采样距离。合成和实数据的实验表明，所提出的方法优于当前最先进的，减轻融合伪像和恢复较小的边缘。

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