提出一种基于迭代自适应滤波原理的端到端深度神经网络。该网络旨在解决由简单透镜的光学结构引起的显著图像边缘模糊问题。利用具有大视场的单个胶合透镜，提出一种像素级去模糊滤波器，该滤波器可有效地适应模糊的空间变化，从而恢复输入图像的模糊特征。通过模拟和在原型摄像机系统上进行的实验验证了所提方法的有效性。

为克服传统质量导向相位展开方法无法正确展开多孤立物体的局限性，提出一种基于区域分割的立体质量导向相位展开方法。该方法将包裹相位分割为多个区域，并为每个孤立区域通过立体匹配算法确立左右相机多视图相位展开初始点，通过质量导向相位展开算法实现多视图孤立物体相位展开。进一步提出了基于区域双目立体展开相位匹配的单频条纹结构光三维（3D）测量，实现了单个频率包裹相位下的多孤立物体3D重建。实验结果表明，所提方法可以实现多孤立物体运动状态的3D重建，在四步相移和单幅条纹图条件下重建标准球的直径平均绝对值偏差分别为0.0135 mm和0.0347 mm。

Photoacoustic imaging (PAI) is a noninvasive emerging imaging method based on the photoacoustic effect, which provides necessary assistance for medical diagnosis. It has the characteristics of large imaging depth and high contrast. However, limited by the equipment cost and reconstruction time requirements, the existing PAI systems distributed with annular array transducers are difficult to take into account both the image quality and the imaging speed. In this paper, a triple-path feature transform network (TFT-Net) for ring-array photoacoustic tomography is proposed to enhance the imaging quality from limited-view and sparse measurement data. Specifically, the network combines the raw photoacoustic pressure signals and conventional linear reconstruction images as input data, and takes the photoacoustic physical model as a prior information to guide the reconstruction process. In addition, to enhance the ability of extracting signal features, the residual block and squeeze and excitation block are introduced into the TFT-Net. For further efficient reconstruction, the final output of photoacoustic signals uses ‘filter-then-upsample’ operation with a pixel-shuffle multiplexer and a max out module. Experiment results on simulated and in-vivo data demonstrate that the constructed TFT-Net can restore the target boundary clearly, reduce background noise, and realize fast and high-quality photoacoustic image reconstruction of limited view with sparse sampling.

中子扩散方程高阶谐波可用于重构堆芯中子注量率分布，但传统源迭代与源修正迭代法求解时的收敛速度慢，计算耗时长。采用隐式重启Arnoldi方法（Implicitly Restarted Arnoldi Method，IRAM）求解本征值问题的中子扩散方程获得谐波数据，通过本征正交分解（Proper Orthogonal Decomposition，POD）与伽辽金（Galerkin）投影相结合的方法构建POD-Galerkin低阶模型，并重构二维稳态TWIGL基准题中子注量率分布。研究结果表明：IRAM方法在求解中子扩散方程的高阶本征值和谐波问题上具有较高的精度；基于POD-Galerkin低阶模型重构中子注量率分布具有较高的保真性与计算效率，有效增值系数与参考解的误差为8.7×10^-5，对角线上快群和热群中子注量率最大相对误差为2.56%，且低阶模型计算用时仅为全阶模型的10.18%。本研究为堆芯中子注量率重构提供了一种可靠且高效的方法，该方法不仅可用于重构稳态时堆芯中子注量率分布，还具有在瞬态情况下预测中子注量率分布的潜力，有望在未来的应用中进一步拓展。

Structured illumination microscopy (SIM) achieves super-resolution (SR) by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction. The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain, it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary, besides, the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts. Here, we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets, and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets (the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function (OTF)). Experiments on reconstructing raw datasets including nonbiological, biological, and simulated samples demonstrate that our method has SR capability, high reconstruction speed, and high robustness to aberration and noise.

Structured illumination microscopy (SIM) is a popular and powerful super-resolution (SR) technique in biomedical research. However, the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio (SNR) of raw images. To obtain high-quality SR images, several raw images need to be captured under high fluorescence level, which further restricts SIM’s temporal resolution and its applications. Deep learning (DL) is a data-driven technology that has been used to expand the limits of optical microscopy. In this study, we propose a deep neural network based on multi-level wavelet and attention mechanism (MWAM) for SIM. Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image, resulting in superior SR images compared to those generated using wide-field images as input data. We also demonstrate that the number of SIM raw images can be reduced to three, with one image in each illumination orientation, to achieve the optimal tradeoff between temporal and spatial resolution. Furthermore, our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms. We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

为探究成像参数对大深度物体聚焦形貌恢复精度的影响规律，明确实际应用中聚焦形貌恢复重建精度不满足要求时成像系统的改进措施，在构建聚焦形貌恢复三维重建精度评价指标的基础上，利用正交实验确定成像参数对聚焦形貌恢复精度影响的主次顺序，重点分析主要和次主要参数对重建精度的影响规律，并揭示最佳成像参数随多聚焦图像采样间距的变化关系。考虑到成像参数的变化实际通过改变系统景深影响聚焦形貌恢复精度，建立了多聚焦图像采样间距与最佳景深之间的经验公式，为系统成像参数的设定提供了理论依据。实验结果表明：焦距和F数是聚焦形貌恢复的主要和次主要影响参数，在给定多聚焦图像采样间距下存在使重建精度最高的最佳焦距和最佳F数，且随着采样间距减小，最佳焦距增大，最佳F数减小；多聚焦图像采样间距与最佳景深之间的经验公式拟合准确率为97.28%，验证准确率为94.76%，可用于最佳景深的计算；采用最佳景深能够显著提升聚焦形貌恢复精度，为大深度物体聚焦形貌恢复精度的提升提供了新途径。