Depth from defocus: A spatial domain approach

A new method named STM is described for determining distance of objects and rapid autofocusing of camera systems. STM uses image defocus information and is based on a new Spatial-Domain Convolution/Deconvolution Transform. The method requires only two images taken with different camera parameters such as lens position, focal length, and aperture diameter. Both images can be arbitrarily blurred and neither of them needs to be a focused image. Therefore STM is very fast in comparison with Depth-from-Focus methods which search for the lens position or focal length of best focus. The method involves simple local operations and can be easily implemented in parallel to obtain the depth-map of a scene. STM has been implemented on an actual camera system named SPARCS. Experiments on the performance of STM and their results on real-world planar objects are presented. The results indicate that the accuracy of STM compares well with Depth-from-Focus methods and is useful in practical applications. The utility of the method is demonstrated for rapid autofocusing of electronic cameras.     

胸部解剖结构回归模型的虚拟双能量X线减影方法

目的 探索从常规X线胸片图像中分割出骨质结构,获取仅含软组织图像的虚拟双能量X线减影的方法,旨在不增加放射剂量的条件下获取高质量的临床肺结节影像诊断效果。方法 首先将肺区自动划分出8个特定解剖结构的子区域：左右侧肺叶的上、中、下部和左右肺门;然后针对每个特定解剖区域,利用从双能量设备获取的标准胸片和其对应的骨质图像对多分辨率的大规模训练人工神经网络（MTANNs）进行训练。训练好后,可以利用该ANN处理获得该解剖结构子区域的虚拟骨质图像。融合从8个多分辨率ANNs输出的骨质图像,融合得到一幅完整的虚拟骨质图像。接下来采用总变分最小化平滑的方法抑制虚拟骨质图像中的噪声,且增强骨骼边缘。最后将虚拟骨质图像从原图中相减获得虚拟软组织图像。结果 用110幅含有肺结节的胸片图像对算法进行了测试,新方法用于常规X线胸片所得虚拟软组织图像可有效地去除原片中骨质结构影像,较清晰地保留肺结节和血管影像,有利于临床肺结节的诊断。采用新方法可使肺结节的正确识别率提高到88%（传统方法识别率为70%）。结论 基于解剖结构的人工神经网络回归模型能有效地分离出骨骼,可以广泛地应用于临床诊断,帮助放射科医生检测出肺结节。     

全像素双核成像技术及应用研究综述

全像素双核(dual-pixel, DP)自动对焦(dual-pixel CMOS auto focus, DP CMOS AF)采用混合检测自动对焦,其在每个像素配备两个光电二极管,使每个像素既参与对焦又参与成像,克服了传统相位检测自动对焦和反差检测自动对焦技术的缺点。根据离焦视差估计图像合焦镜头所需移动的距离,DP自动对焦具有更快的对焦速度和更高的对焦精度,因此广泛应用在手持设备中(如手机相机、单反相机等)。由于全像素双核传感器将每个像素分成两半,该传感器一次拍摄即可得到两幅图像。这两幅图像(全像素双核图像对)可以看做一个具有相同曝光时间和严格校正的小基线立体图像对。该图像对的视差与图像模糊程度相对应,只在离焦区域存在视差。全像素双核传感器不仅用于自动对焦,而且可以用于深度估计、散焦去模糊和反射去除等方面。本文系统地综述了全像素双核传感器的自动对焦、成像原理及研究现状,并进一步展望其未来发展。1)对自动对焦技术进行介绍,对比了传统对焦与全像素双核对焦;2)详细分析了全像素双核传感器的成像原理、成像模型及特点;3)系统地介绍了全像素双核在计算机视觉领域应用的最新进展,从深度估计、反射去除和离焦模糊去除等方面进行全面阐述及分析; 4)适当的数据集是基于深度学习方法的基础,对目前的全像素双核数据集进行了介绍;5)分析了全像素双核在计算机视觉领域面临的挑战与机遇,对未来的全像素双核应用方向进行了讨论与展望。     

微操纵系统自动调焦方法的研究

讨论了在微操纵系统中显微成像的自动调焦方法。首先介绍了一些常用的自动调焦方法，然后重点讲述了清晰度法自动调焦原理．对各种清晰度评价函数进行了分类，并通过理论和实验分析比较了在微操纵系统中各种评价函数的性能，最后确定了适合微操纵系统的清晰度评价函数。     

Design and characterization of precise laser-based autofocusing microscope with reduced geometrical fluctuations

Due to consumer preference for products with ever higher performance, a requirement exists for precise autofocusing microscope systems to perform the inspection process in automated mass production lines. Accordingly, the present study proposes a laser-based microscope system in which a precise autofocusing capability is achieved using a position feedback signal based on the distance L between the geometrical center (X _c , Y _c ) of the image captured by the CCD sensor and the centroid (x _centroid , y _centroid ) of the image. The experimental results show that the proposed system has a positioning accuracy of 2.2 μm and a response time of 1 s given a working range of ±200 μm. The autofocusing performance of the proposed system is thus better than that of a conventional centroid-based system, which typically achieves a positioning accuracy of around 5.2 μm.     

Real-time image restoration for iris recognition systems.

In the field of biometrics, it has been reported that iris recognition techniques have shown high levels of accuracy because unique patterns of the human iris, which has very many degrees of freedom, are used. However, because conventional iris cameras have small depth-of-field (DOF) areas, input iris images can easily be blurred, which can lead to lower recognition performance, since iris patterns are transformed by the blurring caused by optical defocusing. To overcome these problems, an autofocusing camera can be used. However, this inevitably increases the cost, size, and complexity of the system. Therefore, we propose a new real-time iris image-restoration method, which can increase the camera's DOF without requiring any additional hardware. This paper presents five novelties as compared to previous works: 1) by excluding eyelash and eyelid regions, it is possible to obtain more accurate focus scores from input iris images; 2) the parameter of the point spread function (PSF) can be estimated in terms of camera optics and measured focus scores; therefore, parameter estimation is more accurate than it has been in previous research; 3) because the PSF parameter can be obtained by using a predetermined equation, iris image restoration can be done in real-time; 4) by using a constrained least square (CLS) restoration filter that considers noise, performance can be greatly enhanced; and 5) restoration accuracy can also be enhanced by estimating the weight value of the noise-regularization term of the CLS filter according to the amount of image blurring. Experimental results showed that iris recognition errors when using the proposed restoration method were greatly reduced as compared to those results achieved without restoration or those achieved using previous iris-restoration methods.     

基于边缘特征提取的图像清晰度评价函数

图像清晰度评价函数是清晰度法自动调焦技术中用来反馈离焦状态的一个度量工具。调焦系统可以通过对成像清晰度的评价来自动搜索到成像最清晰位置。图像中边缘梯度能量大小反映了一幅图像的清晰状况,根据图像最清晰时图像中边缘梯度最大的特点,设计了一系列基于边缘梯度算子的清晰度评价函数。首先从理论上论证了这一系列函数与其它函数相比更具有鲁棒性、直观性的优点,然后通过实验验证,这一系列函数都符合了调焦函数中单峰性、抗噪性和无偏性的使用要求。     

一种粗精结合的图像反馈自动调焦方法

自动调焦是保证显微成像系统实时获得清晰图像的该装置借助对图像清晰度评价来自动搜索到正焦面.对图像清晰度的评价采用了Krisch边缘梯度函数和高频分量函数相结合的方法,边缘梯度函数用于大步距粗调焦,高频分量函数用于小范围内小步距精调焦.这种方法既提高了效率,又克服了噪声的干扰.通过对不同微器件的多次调焦实验验证,该方法的调焦误差最大达到6um,满足了成像系统的要求.     

The Weibull manifold in low-level image processing: An application to automatic image focusing

In this paper, we introduce a novel framework for low-level image processing and analysis. First, we process images with very simple, difference-based filter functions. Second, we fit the 2-parameter Weibull distribution to the filtered output. This maps each image to the 2D Weibull manifold. Third, we exploit the information geometry of this manifold and solve low-level image processing tasks as minimisation problems on point sets. For a proof-of-concept example, we examine the image autofocusing task. We propose appropriate cost functions together with a simple implicitly-constrained manifold optimisation algorithm and show that our framework compares very favourably against common autofocus methods from literature. In particular, our approach exhibits the best overall performance in terms of combined speed and accuracy.     

Web video thumbnail recommendation with content-aware analysis and query-sensitive matching

In this paper, a unified and adaptive web video thumbnail recommendation framework is proposed, which recommends thumbnails both for video owners and browsers on the basis of image quality assessment, image accessibility analysis, video content representativeness analysis and query-sensitive matching. At the very start, video shot detection is performed and the highest image quality video frame is extracted as the key frame for each shot on the basis of our proposed image quality assessment method. These key frames are utilized as the thumbnail candidates for the following processes. In the image quality assessment, the normalized variance autofocusing function is employed to evaluate the image blur and ensures that the selected video thumbnail candidates are clear and have high image quality. For accessibility analysis, color moment, visual salience and texture are used with a support vector regression model to predict the candidates’ accessibility score, which ensures that the recommended thumbnail’s ROIs are big enough and it is very accessible for users. For content representativeness analysis, the mutual reinforcement algorithm is adopted in the entire video to obtain the candidates’ representativeness score, which ensures that the final thumbnail is representative enough for users to catch the main video contents at a glance. Considering browsers’ query intent, a relevant model is designed to recommend more personalized thumbnails for certain browsers. Finally, by flexibly fusing the above analysis results, the final adaptive recommendation work is accomplished. Experimental results and subjective evaluations demonstrate the effectiveness of the proposed approach. Compared with the existing web video thumbnail generation methods, the thumbnails for video owners not only reflect the contents of the video better, but also make users feel more comfortable. The thumbnails for video browsers directly reflect their preference, which greatly enhances their user experience.