一种基于DFD的自动对焦算法

引入了一种基于DFD的自动对焦算法。通过这种算法,只要给定了两幅不同离焦位置的图像,就可由算法推导出目标物体的正确对焦位置,从而控制镜头完成自动对焦。实验表明,这种算法精度比较高,速度比较快,鲁棒性良好。     

Correction of autofocusing errors due to specimen tilt for automated electron tomography

Transmission electron microscopy images acquired under tilted‐beam conditions experience an image shift as a function of defocus settings – a fact that is exploited as a method for defocus determination in most of the automated tomography data collection systems. Although the method was shown to be highly accurate for a large variety of specimens, we point out that in its original design it can strictly only be applied to images of untilted samples. The application to tilted samples and thus in automated electron tomography is impaired mainly due to a defocus change across the images, resulting in reduced accuracy. In this communication we present a method that can be used to improve the accuracy of the basic autofocusing procedures currently used in systems for automated electron tomography.     

离焦模糊图像的维纳滤波复原研究

微操作中，显微视觉系统获取的图像通常是离焦模糊图像。离焦模糊图像的退化模型可用圆盘函数描述，利用模糊图像无方向性的二阶拉氏微分图像的自相关的负相关峰形成的环形槽的直径等于作为圆盘函数直径的2倍可以确定该函数。对模糊图像进行一次维纳滤波方法得到原图像的估计值，然后利用该初始值求得原图像及噪声的谱密度估值，进而利用这些新获得的信息构成改进的维纳滤波器对退化图像进行第二次滤波。实验表明，该方法计算量小、鉴别精度高、抗噪声能力较强，突出原图像的一些关键细节，提高了图像的复原质量。     

反射式牛顿-施密特光学系统设计

透射式施密特校正板在使用上受到口径和波段的限制,所以需要展开对反射式系统的研究。从三级像差理论出发,并考虑到离焦量引起的焦距变化,推导反射式牛顿-施密特校正板方程式和各种消像差条件,并求解出反射式牛顿-施密特光学系统。材料选择方面反射材料比透镜材料更容易得到,更重要的是反射镜不产生色差。文中得出的公式经具体实例证实是正确的,可以作为光学系统设计的理论基础。     

Integration of multiresolution image segmentation and neural networks for object depth recovery

A novel technique for three-dimensional depth recovery based on two coaxial defocused images of an object with added pattern illumination is presented. The approach integrates object segmentation with depth estimation. Firstly segmentation is performed by a multiresolution based approach to isolate object regions from the background given the presence of blur and pattern illumination. The segmentation has three sub-procedures: image pyramid formation; linkage adaptation; and unsupervised clustering. These maximise the object recognition capability while ensuring accurate position information. For depth estimation, lower resolution information with a strong correlation to depth is fed into a three-layered neural network as input feature vectors and processed using a Back-Propagation algorithm. The resulting depth model of object recovery is then used with higher resolution data to obtain high accuracy depth measurements. Experimental results are presented that show low error rates and the robustness of the model with respect to pattern variation and inaccuracy in optical settings.     

Digital shallow depth-of-field adapter for photographs

This paper proposes a novel method to synthesize shallow depth-of-field images from two input photographs taken with different aperture values. The basic approach is to estimate the depth map of a given scene using a DFD (depth-from-defocus) algorithm and blur an input image according to the estimated depth map. The depth information estimated by DFD contains much noise and error, while the estimation is rather accurate along the edges of the image. To overcome the limitation, we propose a depth map filling algorithm using a set of initial depth maps and a segmented image. After depth map filling, the depth map can be fine tuned by applying segment clustering and user interaction. Since our method blurs an input image according to the estimated depth information, it generates physically plausible result images with shallow depth-of-field. In addition to depth-of-field control, the proposed method can be utilized for digital refocusing and detail control in image stylization.     

Confocal Stereo

We present confocal stereo, a new method for computing 3D shape by controlling the focus and aperture of a lens. The method is specifically designed for reconstructing scenes with high geometric complexity or fine-scale texture. To achieve this, we introduce the confocal constancy property, which states that as the lens aperture varies, the pixel intensity of a visible in-focus scene point will vary in a scene-independent way, that can be predicted by prior radiometric lens calibration. The only requirement is that incoming radiance within the cone subtended by the largest aperture is nearly constant. First, we develop a detailed lens model that factors out the distortions in high resolution SLR cameras (12MP or more) with large-aperture lenses (e.g., f1.2). This allows us to assemble an A×F aperture-focus image (AFI) for each pixel, that collects the undistorted measurements over all A apertures and F focus settings. In the AFI representation, confocal constancy reduces to color comparisons within regions of the AFI, and leads to focus metrics that can be evaluated separately for each pixel. We propose two such metrics and present initial reconstruction results for complex scenes, as well as for a scene with known ground-truth shape. Part of this work was done while the authors were visiting Microsoft Research Asia, in the roles of research intern and Visiting Scholar respectively.     

A Theory of Single-Viewpoint Catadioptric Image Formation

Conventional video cameras have limited fields of view which make them restrictive for certain applications in computational vision. A catadioptric sensor uses a combination of lenses and mirrors placed in a carefully arranged configuration to capture a much wider field of view. One important design goal for catadioptric sensors is choosing the shapes of the mirrors in a way that ensures that the complete catadioptric system has a single effective viewpoint. The reason a single viewpoint is so desirable is that it is a requirement for the generation of pure perspective images from the sensed images. In this paper, we derive the complete class of single-lens single-mirror catadioptric sensors that have a single viewpoint. We describe all of the solutions in detail, including the degenerate ones, with reference to many of the catadioptric systems that have been proposed in the literature. In addition, we derive a simple expression for the spatial resolution of a catadioptric sensor in terms of the resolution of the cameras used to construct it. Moreover, we include detailed analysis of the defocus blur caused by the use of a curved mirror in a catadioptric sensor.     

Real‐Time Defocus Rendering With Level of Detail and Sub‐Sample Blur

This paper presents a GPU‐based rendering algorithm for real‐time defocus blur effects, which significantly improves the accumulation buffering. The algorithm combines three distinctive techniques: (1) adaptive discrete geometric level of detail (LOD), made popping‐free by blending visibility samples across the two adjacent geometric levels; (2) adaptive visibility/shading sampling via sample reuse; (3) visibility supersampling via height‐field ray casting. All the three techniques are seamlessly integrated to lower the rendering cost of smooth defocus blur with high visibility sampling rates, while maintaining most of the quality of brute‐force accumulation buffering.     

基于散焦图像深度测量的一种新方法

提出了基于散焦图像深度测量的一种新方法．该方法采用一台带有远心镜头的CCD摄 像机,沿光轴方向移动摄像机拍摄两副图像,根据所拍摄目标图像的散焦半径与图像的大小 计算目标景物距摄像机的距离．采用远心光学镜头代替普通镜头可使图像的大小与散焦半径 之间的关系简单．该方法融合了图像的大小与散焦半径两种信息,使深度计算更加准确．由 于该方法只需要一台参数固定的CCD摄像机,可以免除图像间的配准和特征点的选取,有利 于实时系统的实现．实验结果表明该方法的有效性．