基于散斑图像的远程振动频率提取方法研究

为了得到远程物体的微米级振动的频率信息,采用基于散斑图像的远程振动频率提取方法,以波长为532nm连续激光器作为光源照射50m远处被测物体表面,通过高速摄像机记录物体表面反射空间内的散斑图样提取物体的微振动频率。采用激光散斑图像的数据处理方法,进行了研究分析和实验验证,利用长焦镜头对远程散斑图像进行适当散焦处理,然后提取视频散斑图像间互相关系数峰值点与原点之间的矢量大小来获取散斑的变化信息,同时对一定宽频率范围内的振动频率进行提取。结果表明,用该方法得到的激光散斑远程微振动频率的提取准确度达99.22%。该数据结果为激光散斑应用于远程频率振动检测提供了一定的论证依据。     

基于深度学习的显微离焦图像法颗粒深度测量

显微成像条件下的三维流场测量是微通道流动等研究的基础,其难点在于颗粒深度位置的测量。由于显微镜头景深极小,成像时通道内大部分颗粒处于离焦状态。本文首先基于几何光学原理分析了显微成像前后离焦 不对称的特点,随后基于Inception V3卷积神经网络搭建了颗粒深度预测模型,并通过光线追踪方法生成粒径 1~10μm的10种颗粒在深度范围-50～50μm内的仿真显微图像,应用深度预测模型对其进行训练及预测,颗粒深度预测结果显示1～3μm颗粒的相对误差在±13%以内,4～10μm颗粒的相对误差小于±5%。最后在微通道中拍摄粒径分别为2.6μm和5μm的聚苯乙烯微球在深度范围-50～50μm内的显微图像,应用同一深度预测模型进行训练及预测,两种颗粒深度预测结果的相对误差分别为小于±15%和±5%。结果表明,所提出的基于深度学习的显微离焦图像法能够有效测量微通道内颗粒深度位置,为图像法流场测量技术增加了新的思路。     

基于离焦估计的对焦速度的提高方法

提出一种新的自动对焦准焦点定位方法。首先在3个位置估算离焦量,直接驱动镜头至准焦位置附近,用小步长进行搜索,找到最佳对焦位置。实验结果表明,该方法不需要对成像系统进行精确校准,在保持对焦深度(DFF)法的精度优势的同时,将对焦速度提高了37%。     

BaMBNet: A Blur-Aware Multi-Branch Network for Dual-Pixel Defocus Deblurring

Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography. It is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional methods. Due to its great breakthrough in low-level tasks, convolutional neural networks (CNNs) have been introduced to the defocus deblurring problem and achieved significant progress. However, previous methods apply the same learned kernel for different regions of the defocus blurred images, thus it is difficult to handle nonuniform blurred images. To this end, this study designs a novel blur-aware multi-branch network (BaMBNet), in which different regions are treated differentially. In particular, we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel (DP) data, which measures the defocus disparity between the left and right views. Based on the assumption that different image regions with different blur amounts have different deblurring difficulties, we leverage different networks with different capacities to treat different image regions. Moreover, we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper branch. In this way, we can expect to maintain the information of the clear regions well while recovering the missing details of the blurred regions. Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art (SOTA) methods. For the dual-pixel defocus deblurring (DPD)-blur dataset, the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio (PSNR) and reduces learnable parameters by 85%. The details of the code and dataset are available at https://github.com/junjun-jiang/BaMBNet.      

集成成像技术的像质评价分析

为了研究集成成像非共轭面的成像质量,根据光线追迹原理分析透镜阵列的成像过程,指出三维成像原理并给出不同离焦量下的轴截面成像分辨率;从波动光学角度分析包含离焦波像差的透镜阵列光学传递(OTF),得到集成成像系统的非共轭面调制传递函数(MTF)并指出与单透镜共轭面成像的联系.根据非共轭面分辨率给出符合视觉要求的显示深度计算方法.采用投影仪与菲涅尔透镜搭建显示面积为100×750 mm2的集成成像显示系统,实验结果表明,集成成像轴截面成像分辨率在离焦量增大的情况下迅速下降,非共轭面像质是制约集成成像显示效果的重要因素之一.     

Defocus from depth for defocus measurement

A novel method is proposed for defocus map estimation. It is based on the defocus origin that is essentially the reverse of depth from defocus (DFD). The main relations among image defocus, sensor defocus, and scene defocus for an imaging system are introduced. A defocus map is deduced from the depth map and the depth map is derived from the disparity map. The full disparity map can be reconstructed using an image-matching method and our clustering segmentation algorithm. Experimental results for an interior scene and an outdoor scene demonstrate that our method is effective in defocus measurement.     

点源离焦图像位相复原方法

为了实现利用点源传递函数测试仪器在测量可见光镜头光学传递函数的同时测出镜头出瞳的波前,采用了一种基于点源目标离焦图像的位相复原算法.该方法无需改变点源传递函数测试仪器的硬件设置,直接利用在传递函数测试过程中,通过改变位于镜头后焦面附近的显微镜与CCD的位置所采集的多幅离焦图像及改进的GS算法实现离焦面与镜头出瞳面光场的...     

由粗到精的多尺度散焦模糊检测

目的 散焦模糊检测致力于区分图像中的清晰与模糊像素，广泛应用于诸多领域，是计算机视觉中的重要研究方向。待检测图像含复杂场景时，现有的散焦模糊检测方法存在精度不够高、检测结果边界不完整等问题。本文提出一种由粗到精的多尺度散焦模糊检测网络，通过融合不同尺度下图像的多层卷积特征提高散焦模糊的检测精度。方法 将图像缩放至不同尺度，使用卷积神经网络从每个尺度下的图像中提取多层卷积特征，并使用卷积层融合不同尺度图像对应层的特征；使用卷积长短时记忆（convolutional long-short term memory，Conv-LSTM）层自顶向下地整合不同尺度的模糊特征，同时生成对应尺度的模糊检测图，以这种方式将深层的语义信息逐步传递至浅层网络；在此过程中，将深浅层特征联合，利用浅层特征细化深一层的模糊检测结果；使用卷积层将多尺度检测结果融合得到最终结果。本文在网络训练过程中使用了多层监督策略确保每个Conv-LSTM层都能达到最优。结果 在DUT （Dalian University of Technology）和CUHK （The Chinese University of Hong Kong）两个公共的模糊检测数据集上进行训练和测试，对比了包括当前最好的模糊检测算法BTBCRL （bottom-top-bottom network with cascaded defocus blur detection map residual learning），DeFusionNet （defocus blur detection network via recurrently fusing and refining multi-scale deep features）和DHDE （multi-scale deep and hand-crafted features for defocus estimation）等10种算法。实验结果表明：在DUT数据集上，本文模型相比于DeFusionNet模型，MAE （mean absolute error）值降低了38.8%，F_0.3值提高了5.4%；在CUHK数据集上，相比于LBP （local binary pattern）算法，MAE值降低了36.7%，F_0.3值提高了9.7%。通过实验对比，充分验证了本文提出的散焦模糊检测模型的有效性。结论 本文提出的由粗到精的多尺度散焦模糊检测方法，通过融合不同尺度图像的特征，以及使用卷积长短时记忆层自顶向下地整合深层的语义信息和浅层的细节信息，使得模型在不同的图像场景中能得到更加准确的散焦模糊检测结果。     

Range measurement from defocus gradient

Images taken of 3D objects are generally defocused. Depth-from-focus techniques use the defocus information to determine range. However, quantitative measurement of focus is difficult and requires accurate modeling of the point-spread function (PSF). We describe a new method that determines depth using the symmetry and smoothness of focus gradient with respect to the focus position. The technique is passive and uses a monocular imaging system. The performance for estimating range is experimentally demonstrated.     

粉末冶金材料激光深熔焊接工艺研究

用LSM 2 40全自动SLAB激光焊接机对Fe基、Ni基、Co基3种粉末材料进行焊接实验。研究了焦点位置、焊接速度、焊接线能量对粉末材料激光焊接焊缝质量的影响。结果表明,Ni对离焦量最敏感。并得到了焊接熔深H与输入线能量W的数学表达式。