Robust artifact-free high dynamic range imaging of dynamic scenes

The irradiance range of the real-world scene is often beyond the capability of digital cameras. Therefore, High Dynamic Range (HDR) images can be generated by fusing images with different exposure of the same scene. However, moving objects pose the most severe problem in the HDR imaging, leading to the annoying ghost artifacts in the fused image. In this paper, we present a novel HDR technique to address the moving objects problem. Since the input low dynamic range (LDR) images captured by a camera act as static linear related backgrounds with moving objects during each individual exposures, we formulate the detection of foreground moving objects as a rank minimization problem. Meanwhile, in order to eliminate the image blurring caused by background slightly change of LDR images, we further rectify the background by employing the irradiances alignment. Experiments on image sequences show that the proposed algorithm performs significant gains in synthesized HDR image quality compare to state-of-the-art methods.

     

打火机火焰图像分析中的快速自动曝光算法

为满足打火机火焰图像采集时对曝光的不同要求,提出一种快速自动曝光算法。根据火焰亮度计算曝光时间,并利用该曝光时间与图像灰度之间的关系,使用二分法确定曝光时间的范围。实验结果表明,该算法在不同情况下能正确、快速地对打火机火焰的曝光时间进行调节,使曝光正确率达到97.2%。     

Evaluation of a digital camera image applied to PCB inspection

Rapid advancement and widespread digital camera applications have made it possible to replace charge‐coupled device (CCD) cameras in automatic inspections for industrial applications. However, most digital camera applications using the automatic exposure mode may not be effective in some of the inspection environments. The reflection from a board surface in printed circuit board (PCB) inspections is one such problem area. The objective of this study is to develop a methodology to evaluate the effectiveness of using digital cameras for inspection. The indices used for evaluating digital camera image quality are the perceived image quality, the visual resolution, and the noise. An experiment was designed and conducted to determine the optimal camera parameter combination for attaining the best image quality. The desirability function was used to compare various digital camera parameter settings in considering three image quality indices for selecting the best camera‐operating conditions. Based on the developed model and the subjective image quality index, the overall image quality improved 9.4% and 13.86%, respectively. The developed methodology can be used to: (a) determine the digital camera image quality, (b) provide an improved model for determining the automatic exposure setting for digital camera designers, and (c) adjust the digital camera parameters for automatic inspection. © 2008 Wiley Periodicals, Inc.     

自适应成本量的抗遮挡光场深度估计算法

目的 光场相机通过一次成像同时记录场景的空间信息和角度信息,获取多视角图像和重聚焦图像,在深度估计中具有独特优势。遮挡是光场深度估计中的难点问题之一,现有方法没有考虑遮挡或仅仅考虑单一遮挡情况,对于多遮挡场景点,方法失效。针对遮挡问题,在多视角立体匹配框架下,提出了一种对遮挡鲁棒的光场深度估计算法。方法 首先利用数字重聚焦算法获取重聚焦图像,定义场景的遮挡类型,并构造相关性成本量。然后根据最小成本原则自适应选择最佳成本量,并求解局部深度图。最后利用马尔可夫随机场结合成本量和平滑约束,通过图割算法和加权中值滤波获取全局优化深度图,提升深度估计精度。结果 实验在HCI合成数据集和Stanford Lytro Illum实际场景数据集上展开,分别进行局部深度估计与全局深度估计实验。实验结果表明,相比其他先进方法,本文方法对遮挡场景效果更好,均方误差平均降低约26.8%。结论 本文方法能够有效处理不同遮挡情况,更好地保持深度图边缘信息,深度估计结果更准确,且时效性更好。此外,本文方法适用场景是朗伯平面场景,对于含有高光的非朗伯平面场景存在一定缺陷。     

Power over Cable：整合数据传输与供电,单一缆线解决工业视觉应用的需求

所有的工业机器视觉设备都需要依赖图像数据传输与电源供应,而且大多数的工业相机都需要使用两条缆线,一条用于传输数据,另一条则是电源线,这意味着使用者必须多花一倍安装与维护缆线的成本,同时缆线故障的机率也增加一倍。随着工业机器视觉市场从模拟跨入数字相机后,如何通过＂Power over Cable＂供电型接口简化安装、节省空间以及降低成本,成为工业机器视觉应用中最新的话题。     

Camera self-calibration with varying intrinsic parameters by an unknown three-dimensional scene

This work proposes a method of camera self-calibration having varying intrinsic parameters from a sequence of images of an unknown 3D object. The projection of two points of the 3D scene in the image planes is used with fundamental matrices to determine the projection matrices. The present approach is based on the formulation of a nonlinear cost function from the determination of a relationship between two points of the scene and their projections in the image planes. The resolution of this function enables us to estimate the intrinsic parameters of different cameras. The strong point of the present approach is clearly seen in the minimization of the three constraints of a self-calibration system (a pair of images, 3D scene, any camera): The use of a single pair of images provides fewer equations, which minimizes the execution time of the program, the use of a 3D scene reduces the planarity constraints, and the use of any camera eliminates the constraints of cameras having constant parameters. The experiment results on synthetic and real data are presented to demonstrate the performance of the present approach in terms of accuracy, simplicity, stability, and convergence.     

数字摄像机的高动态范围曝光算法及实现

在机器视觉领域,许多应用对摄像机的动态范围提出了苛刻的要求.对数曲线响应传感器的出现为图像动态范围的提升奠定了硬件基础.然而目前针对该传感器的曝光控制均需人工操作评估,自动性、实时性差.为了解决这个问题,提出了一种基于对数曲线响应传感器的高动态范围自动曝光算法,并基于现场可编程门阵列(FPGA)并行计算在硬件上实现该算...     

Computationally efficient image deblurring using low rank image approximation and its GPU implementation

This paper presents a computationally efficient technique for reduction of blur caused by handshakes in images captured by mobile devices. This technique uses a short-exposure or a low-exposure image that is captured at the same time a normal or auto-exposure image is captured. The short-exposure image is enhanced by utilizing low rank image approximation of the auto-exposure image without requiring any user specified parameters. Based on the three quantitative measures of image quality, it is shown that this technique outperforms similar techniques used for image deblurring while it also offers computational efficiency. A GPU implementation of this technique is also reported.     

融合事件相机的视觉场景识别

目的 传统视觉场景识别（visual place recognition,VPR）算法的性能依赖光学图像的成像质量,因此高速和高动态范围场景导致的图像质量下降会进一步影响视觉场景识别算法的性能。针对此问题,提出一种融合事件相机的视觉场景识别算法,利用事件相机的低延时和高动态范围的特性,提升视觉场景识别算法在高速和高动态范围等极端场景下的识别性能。方法 本文提出的方法首先使用图像特征提取模块提取质量良好的参考图像的特征,然后使用多模态特征融合模块提取查询图像及其曝光区间事件信息的多模态融合特征,最后通过特征匹配查找与查询图像最相似的参考图像。结果 在MVSEC（multi-vehicle stereo event camera dataset）和RobotCar两个数据集上的实验表明,本文方法对比现有视觉场景识别算法在高速和高动态范围场景下具有明显优势。在高速高动态范围场景下,本文方法在MVSEC数据集上相较对比算法最优值在召回率与精度上分别提升5.39%和8.55%,在Robot‐Car数据集上相较对比算法最优值在召回率与精度上分别提升3.36%与4.41%。结论 本文提出了融合事件相机的视觉场景识别算法,利用了事件相机在高速和高动态范围场景的成像优势,有效提升了视觉场景识别算法在高速和高动态范围场景下的场景识别性能。     

Omnivergent Stereo

The notion of a virtual camera for optimal 3D reconstruction is introduced. Instead of planar perspective images that collect many rays at a fixed viewpoint, omnivergent cameras collect a small number of rays at many different viewpoints. The resulting 2D manifold of rays is arranged into two multiple-perspective images for stereo reconstruction. We call such images omnivergent images, and the process of reconstructing the scene from such images omnivergent stereo. This procedure is shown to produce 3D scene models with minimal reconstruction error, due to the fact that for any point in the 3D scene, two rays with maximum vergence angle can be found in the omnivergent images. Furthermore, omnivergent images are shown to have horizontal epipolar lines, enabling the application of traditional stereo matching algorithms, without modification. Three types of omnivergent virtual cameras are presented: spherical omnivergent cameras,center-strip cameras and dual-strip cameras.     

Photorealistic Scene Reconstruction by Voxel Coloring

A novel scene reconstruction technique is presented, different from previous approaches in its ability to cope with large changes in visibility and its modeling of intrinsic scene color and texture information. The method avoids image correspondence problems by working in a discretized scene space whose voxels are traversed in a fixed visibility ordering. This strategy takes full account of occlusions and allows the input cameras to be far apart and widely distributed about the environment. The algorithm identifies a special set of invariant voxels which together form a spatial and photometric reconstruction of the scene, fully consistent with the input images. The approach is evaluated with images from both inward-facing and outward-facing cameras.     

Camera and visual veiling glare in HDR images

Abstract— High‐dynamic‐range (HDR) images are superior to conventional images. The experiments in this paper measure camera and human responses to calibrated HDR test targets. We calibrated a 4.3‐log‐unit test target, with minimal and maximal glare from a changeable surround. Glare is an uncontrolled spread of an image‐dependent fraction of scene luminance in cameras and in the eye. We use this standard test target to measure the range of luminances that can be captured on a camera's image plane. Further, we measure the appearance of these test luminance patches. We discuss why HDR is better than conventional imaging, despite the fact the reproduction of luminance is inaccurate.     

Tri-view morphing

This paper presents an efficient image-based approach to navigate a scene based on only three wide-baseline uncalibrated images without the explicit use of a 3D model. After automatically recovering corresponding points between each pair of images, an accurate trifocal plane is extracted from the trifocal tensor of these three images. Next, based on a small number of feature marks using a friendly GUI, the correct dense disparity maps are obtained by using our trinocular-stereo algorithm. Employing the barycentric warping scheme with the computed disparity, we can generate an arbitrary novel view within a triangle spanned by three camera centers. Furthermore, after self-calibration of the cameras, 3D objects can be correctly augmented into the virtual environment synthesized by the tri-view morphing algorithm. Three applications of the tri-view morphing algorithm are demonstrated. The first one is 4D video synthesis, which can be used to fill in the gap between a few sparsely located video cameras to synthetically generate a video from a virtual moving camera. This synthetic camera can be used to view the dynamic scene from a novel view instead of the original static camera views. The second application is multiple view morphing, where we can seamlessly fly through the scene over a 2D space constructed by more than three cameras. The last one is dynamic scene synthesis using three still images, where several rigid objects may move in any orientation or direction. After segmenting three reference frames into several layers, the novel views in the dynamic scene can be generated by applying our algorithm. Finally, the experiments are presented to illustrate that a series of photo-realistic virtual views can be generated to fly through a virtual environment covered by several static cameras.     

基于光圈控制的自动曝光算法研究

曝光是评价成像质量的关键参数之一.由于受光敏感度、自动增益等影响,光圈口径大小与亮度呈非线性关系,通过把图像分块提取亮度和测定光圈口径与亮度的关系,设计了光圈控制的非线性自动曝光算法.实验表明:采用光圈控制的非线性自动曝光算法能够实现准确、快速的曝光,并使光圈控制具有良好的稳定性.     

自适应色彩矫正图像增强算法仿真研究

研究图像增强优化问题,由于大量的图像由于拍照抖动产生噪声,造成图像模糊问题,而传统的去除图像运动模糊的算法,具有计算复杂度过高和特定的假设条件的局限,为了改善图像视觉效果,提出了一种改进的计算量小的自适应矫正图像增强算法,利用模糊图像作为参考,对欠曝光图像进行非线性自适应色调矫正。首先利用非线性函数对不同通道颜色进行调节,然后使用自适应方法对亮度进行矫正仿真,得到最终清晰图像。仿真结果表明改算法可以有效增强图像,改善了图像的质量,具有一定的实际应用价值。     

Accidental Pinhole and Pinspeck Cameras

We identify and study two types of “accidental” images that can be formed in scenes. The first is an accidental pinhole camera image. The second class of accidental images are “inverse” pinhole camera images, formed by subtracting an image with a small occluder present from a reference image without the occluder. Both types of accidental cameras happen in a variety of different situations. For example, an indoor scene illuminated by natural light, a street with a person walking under the shadow of a building, etc. The images produced by accidental cameras are often mistaken for shadows or interreflections. However, accidental images can reveal information about the scene outside the image, the lighting conditions, or the aperture by which light enters the scene.     

Effective demosaicking algorithm based on edge property for color filter arrays

Under the influence of digitization, digital still cameras (DSC) are becoming prevalent in recent years. The image capturing and file storing process of a digital still camera involves multiple image processing and precise corrective calculations. In order to reduce cost and volume, a digital still camera usually utilizes only one sensor and color filter array (CFA) for capturing images, and reconstructs a corresponding full-color image using a color interpolation method. Demosaicking is the first step of image processing of digital still cameras and has been integrated into the design of a variety of digital still cameras. If noise and blurred edges exist from the onset of image reconstruction, a post-processing can do little to improve the quality of the reconstructed image. A demosaicking method is proposed in this paper to prevent the occurrence of color artifacts. By detecting the edge characteristics of a digital image, accurate weights can be obtained for image interpolation, before refinement is made in post-processing. After comparing the experiment results of this paper with those of previously proposed methods, it is found that the proposed method can effectively reduce color artifacts and enhance image quality.     

Three-dimensional scene flow

Just as optical flow is the two-dimensional motion of points in an image, scene flow is the three-dimensional motion of points in the world. The fundamental difficulty with optical flow is that only the normal flow can be computed directly from the image measurements, without some form of smoothing or regularization. In this paper, we begin by showing that the same fundamental limitation applies to scene flow; however, many cameras are used to image the scene. There are then two choices when computing scene flow: 1) perform the regularization in the images or 2) perform the regularization on the surface of the object in the scene. In this paper, we choose to compute scene flow using regularization in the images. We describe three algorithms, the first two for computing scene flow from optical flows and the third for constraining scene structure from the inconsistencies in multiple optical flows.     

Color balancing of digital photos using simple image statistics

The great diffusion of digital cameras and the widespread use of the internet have produced a mass of digital images depicting a huge variety of subjects, generally acquired by unknown imaging systems under unknown lighting conditions. This makes color balancing, recovery of the color characteristics of the original scene, increasingly difficult. In this paper, we describe a method for detecting and removing a color cast (i.e. a superimposed color due to lighting conditions, or to the characteristics of the capturing device), from a digital photo without any a priori knowledge of its semantic content. First a cast detector, using simple image statistics, classifies the input images as presenting no cast, evident cast, ambiguous cast, a predominant color that must be preserved (such as in underwater images or single color close-ups) or as unclassifiable. A cast remover, a modified version of the white balance algorithm, is then applied in cases of evident or ambiguous cast. The method we propose has been tested with positive results on a data set of some 750 photos.     

Adaptive height-modified histogram equalization and chroma correction in YCbCr color space for fast backlight image compensation

Automatic exposure controls in commercially available cameras often encounter difficulties in capturing scenes with backlight luminance which dominates the entire image. An Adaptive Height-Modified Histogram Equalization (AHMHE) algorithm is proposed as a compensation technique for backlight images. It simultaneously enhances contrast in both the dark and the bright areas without creating regions of degraded local contrast. Moreover AHMHE is an adaptive algorithm: thus it requires minimal user input, and its reduced computational requirement makes it suitable for real-time application. In addition to AHMHE, a chroma correction technique was applied to chroma components in the YCbCr color space to produce more vivid color images. A series of subjective and index evaluations were conducted to measure the resultant image quality improvements by the AHMHE and the chroma correction algorithms.