论文标题
基于空间变化的CNN点扩展功能估计光学显微镜中盲卷积和深度估计的估计
Spatially-Variant CNN-based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy
论文作者
论文摘要
光学显微镜是生物学和医学的重要工具。将单个镜头中的薄成像（不依赖更复杂的分段设置）中的薄成像进行成像仍然具有挑战性，因为高分辨率显微镜带来的浅水深度会导致图像区域，并使深度本地化和定量图像解释变得困难。 在这里，我们提出了一种方法，该方法通过局部估算图像失真，同时共同估计对象距离与焦平面的距离来改善此类物体的光学显微镜图像的分辨率。具体而言，我们使用卷积神经网络（CNN）估算了空间变化点传播函数（PSF）模型的参数，该卷积神经网络（CNN）不需要仪器特定的校准。我们的方法从图像本身中恢复PSF参数，在理想条件下达到0.99的平方Pearson相关系数，同时保持对物体旋转，照明变化或光子噪声的坚固耐用。当恢复后的PSF与空间变化和正规化的Richardson-Lucy反卷积算法一起使用时，与其他盲型反卷积技术相比，我们观察到高达2.1 dB的信噪比更好。经过显微镜特异性校准后，我们进一步证明了恢复的PSF模型参数允许使用工程PSF时的精度为2微米的精度估算表面深度，并且超过了扩展范围。我们的方法开辟了多种可能性，以增强对光学设置的先验知识的最少需求，以增强非燃料对象的图像。
Optical microscopy is an essential tool in biology and medicine. Imaging thin, yet non-flat objects in a single shot (without relying on more sophisticated sectioning setups) remains challenging as the shallow depth of field that comes with high-resolution microscopes leads to unsharp image regions and makes depth localization and quantitative image interpretation difficult. Here, we present a method that improves the resolution of light microscopy images of such objects by locally estimating image distortion while jointly estimating object distance to the focal plane. Specifically, we estimate the parameters of a spatially-variant Point-Spread function (PSF) model using a Convolutional Neural Network (CNN), which does not require instrument- or object-specific calibration. Our method recovers PSF parameters from the image itself with up to a squared Pearson correlation coefficient of 0.99 in ideal conditions, while remaining robust to object rotation, illumination variations, or photon noise. When the recovered PSFs are used with a spatially-variant and regularized Richardson-Lucy deconvolution algorithm, we observed up to 2.1 dB better signal-to-noise ratio compared to other blind deconvolution techniques. Following microscope-specific calibration, we further demonstrate that the recovered PSF model parameters permit estimating surface depth with a precision of 2 micrometers and over an extended range when using engineered PSFs. Our method opens up multiple possibilities for enhancing images of non-flat objects with minimal need for a priori knowledge about the optical setup.
