Perception-based seam cutting for image stitching

Image stitching is still challenging in consumer-level photography due to imperfect image captures. Recent works show that seam-cutting approaches can effectively relieve the artifacts generated by local misalignment. Normally, the seam-cutting approach is described in terms of energy minimization. However, few of existing methods consider the human perception in their energy functions, which sometimes causes that there exists another seam that is perceptually better than the one with the minimum energy. In this paper, we propose a novel perception-based seam-cutting approach that considers the nonlinearity and the nonuniformity of human perception into the energy minimization. Our method uses a sigmoid metric to characterize the perception of color discrimination and a saliency weight to simulate that the human eye inclines to pay more attention to the salient objects. In addition, our approach can be easily integrated into other stitching pipelines. Representative experiments demonstrate substantial improvements over the conventional seam-cutting approach.     

一种具视差鲁棒性的图像拼接算法

传统的图像拼接算法对视差较为敏感,要求不同帧的拍照位置必须相同。为了解决具视差的图像拼接问题,在对基于平滑变化的仿射拼接算法的分析研究上提出改进算法。通过设计合理规则筛选有效的特征点,再采用非局部平均滤波求解任意点仿射变换参数,最后设计特定的插值方法实现图像融合,从而提高传统拼接算法在时间和拼接效果上的不足。实验结果证明改进算法对具视差变化的图像拼接有较高鲁棒性,可高速实现较好质量的图像拼接。     

Fast image reconstruction network in image stitching

Compared with the traditional feature-based image stitching algorithm, the free-view image stitching algorithm based on deep learning has the advantages of fast stitching speed and good effect. However, these algorithms still cannot achieve real-time splicing speed. For the image reconstruction stage, we redesign a new fast image reconstruction network. This network is designed based on ShuffleNet, and the new network structure and loss function will reduce the time required for image reconstruction. In addition, this network can also reduce the performance loss after the network is lightweight. It is proved by experiments that the fast image reconstruction network can realize real-time high-resolution free-view image reconstruction.     

结合边缘检测的快速SIFT图像拼接方法

为了对身管内壁序列图像进行精确配准与融合拼接,得到大视场高分辨率待检测图像,根据图像特点提出了一种结合边缘检测的快速SIFT图像拼接方法。该方法充分考虑待处理图像的特点,首先对感兴趣区域的图像进行边缘检测,分割出细节信息最丰富的子区域,再对分割出的子区域提取SIFT特征点并进行配准。然后,使用基于Sigmoid型函数权重的图像融合方法,实现图像之间的无缝融合,最大程度地保证了融合图像的清晰度和细节信息的完整性。实验结果表明:改进的方法和传统SIFT算法相比,在特征点提取阶段的平均效率提高了80%左右,且整体配准阶段的效率也有较大提高。图像融合结果在主观评价和各种客观评价值上都能满足工程实际需求。     

快速稳健的背景运动场估计方法

从运动背景中检测与跟踪运行目标是计算机视觉研究领域的热点，其中的困难之一是消除运动背景的影响，提出一种基于均匀稀疏采样减少仿射变换参数估计计量的快速方法，并且利用Huber函数对误差的敏感度不同的特性，使得背景的仿射参数估计过程只对背景像素点敏感，减少了前景运动物体对参数估计造成的误差，提高参数估计的精度。     

Fast motion estimation using reliability weighted robust search

The displaced frame difference (DFD) is the most commonly used search criterion in motion estimation optimisation methods. Using a simple reliability function it is possible to speed up scale estimation required when using robust influence functions and it is shown that the search also experiences speed gains     

基于区间估计的快速单幅图像去雾方法

单幅图像去雾是一个病态反问题。为了解决这个问题,对于大气光强度和介质传输率的估计已进行了大量研究。本文从新的角度提出一种简单但有效的基于区间估计的单幅图像快速去雾方法。该方法从大气散射模型出发,首先,通过对大气光强度和介质传输率取值范围的讨论,减小区间估计的范围。其次,通过快速联合双边滤波和中值滤波对介质传输率进行边缘优化,并利用大气光强度与暗通道差值绝对值大小对介质传输率中明亮区域进行判断和修正。最后,通过色调调整对复原图像进行整体优化。实验结果表明,与几种典型的图像去雾方法比较,本文方法明显提高了算法运算速度,并获得了较好的复原效果。     

Fast code-rate optimization for robust image transmission over lossy packet networks

In this paper, we propose an efficient method for the allocation of Reed-Solomon codes to source symbols, for unequal loss protection. The proposed formulation recasts the multivariate optimization problem into a univariate one, dramatically reducing the computational complexity. Results are shown for image transmission over lossy packet networks, employing the JPEG2000 and SPIHT encoders. The proposed algorithm exhibits performance equivalent to previous methods, while providing a significant complexity reduction.     

一种用于图像拼接的改进ORB算法

针对Oriented FAST and Rotated BRIEF(ORB)算法缺少尺度不变特性,误匹配率高,易造成图像拼接质量差等问题,本文提出一种用于图像拼接的改进ORB算法。首先使用一种基于尺度不变的特征检测算法对图像进行特征点检测,然后用ORB描述算法对特征点进行特征描述,用ORB匹配算法进行粗匹配,再用双向匹配和Random Sampling Coherence(RANSAC)算法对匹配点进行精匹配和提纯,进一步提高其正确率,最后使用渐入渐出加权融合完成图像拼接。实验结果表明,本文方法在图像的缩放和旋转、模糊、光照强度、拍摄视角等情况下都具有优秀的鲁棒性和稳定性,是一种实时性强、准确度高、拼接质量优秀的图像拼接方法。     

手动PCB外观检查机的图像采集与拼接

利用Matrox图像采集卡,采用异步双缓存方法满足了PCB外观检查机图像采集和实时处理的需要.并针对PCB外观检查机采集的图像数据大精度高,需要实时拼接的特点,提出了将GDl+图形设备接口库与ActiveMil库采用结合应用的图像拼接方法.测试实例表明,图像采集速度提高了30%,非常适合应用在PCB外观检查机上,并且由...     

航空摇摆图像组的配准拼接算法

随着航空遥感应用的不断扩大和要求的不断提高,如何扩大航空遥感拍摄的视场成为越来越关注的问题,改进航空遥感相机的拍摄方式成为扩大拍摄视场的一种重要方法.航空摇摆相机将固定式拍摄方式改为运动式拍摄方式,将每一个拍摄点拍一幅图像改为每一个拍摄点拍三幅成像平面不同但具有部分区域重叠的图像,然后通过将这三幅图像组成的图像组拼接起来得到视场更大的图像.深入研究了航空摇摆相机的摇摆模型,提出了一种摇摆图像组的配准拼接算法.实验结果表明:该摇摆图像组配准拼接算法具有良好的配准拼接效果.     

Fast robust correlation.

A new, fast, statistically robust, exhaustive, translational image-matching technique is presented: fast robust correlation. Existing methods are either slow or non-robust, or rely on optimization. Fast robust correlation works by expressing a robust matching surface as a series of correlations. Speed is obtained by computing correlations in the frequency domain. Computational cost is analyzed and the method is shown to be fast. Speed is comparable to conventional correlation and, for large images, thousands of times faster than direct robust matching. Three experiments demonstrate the advantage of the technique over standard correlation.     

Seamless image stitching by minimizing false edges.

Various applications such as mosaicing and object insertion require stitching of image parts. The stitching quality is measured visually by the similarity of the stitched image to each of the input images, and by the visibility of the seam between the stitched images. In order to define and get the best possible stitching, we introduce several formal cost functions for the evaluation of the stitching quality. In these cost functions the similarity to the input images and the visibility of the seam are defined in the gradient domain, minimizing the disturbing edges along the seam. A good image stitching will optimize these cost functions, overcoming both photometric inconsistencies and geometric misalignments between the stitched images. We study the cost functions and compare their performance for different scenarios both theoretically and practically. Our approach is demonstrated in various applications including generation of panoramic images, object blending and removal of compression artifacts. Comparisons with existing methods show the benefits of optimizing the measures in the gradient domain.     

面向机载分布式光电的图像拼接算法

随着航空无人系统技术的迅猛发展,分布式机载图像拼接技术已成为备受瞩目的研究领域。本文针对分布式机载图像拼接中存在的视差大和空间几何变换关系复杂等问题,在APAP图像拼接算法基础上提出了一种改进算法。该算法采用变形处理、线性化单应平滑外推至全局性变换以及网格划分法等方法,有效消除了模糊重影、减少边缘处投影失真,提高算法运行效率。在多个场景下的实验中,该算法表现出更小的对齐误差和更高的图像质量指标,包括均方根误差、峰值信噪比、结构相似度和图像熵等。在进行大规模图像拼接时,该改进算法能够实现154张图像的大规模拼接,得到10 k×10 k的高分辨率全景图像,拼接耗时为138 s。因此,该改进算法具有重要的实际应用价值,可用于分布式机载图像拼接的实际应用中。     

实现图像稳定的快速算法

从舰载光电系统的图像稳定的要求出发,介绍了稳像技术的种类;分析了电子稳像的原理、算法。提出一种新算法,实现了电子图像稳定。该算法利用最小均方误差和块间运动矢量的空间相关性作为匹配函数,采用四步搜索法计算运动矢量,并和全搜索算法得到的运动矢量进行比较。实验表明,该算法能较好地实现图像序列的稳定。     

基于区域特征与灰度交叉相关的序列图像拼接

针对具有平移、旋转、缩放变换的序列图像连续拼接,提出一种将基于区域特征的配准算法和基于灰度交叉相关的配准算法相结合的拼接算法。该算法用迭代阈值分割算法提取区域．利用区域特征进行配准,建立初始的同名区域对;然后以同名区域对的质心点作为特征点,基于图像的灰度信息,选择交叉相关准则作为度量;最后得到图像间的精确变换关系,实现序列图像的拼接。实验结果表明,算法配准率高,鲁棒性强。     

Fast and robust multiframe super resolution

Super-resolution reconstruction produces one or a set of high-resolution images from a set of low-resolution images. In the last two decades, a variety of super-resolution methods have been proposed. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their short-comings. We propose an alternate approach using L1 norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation and results in images with sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods.     

A new image registration method robust to noise

Image registration is a fundamental task in many image processing applications. In this paper, we estimate the translation, rotation, and scaling parameters between a reference image and a distorted image in order to register them. We use the ratio of means to estimate the scaling factor, the Radon transform to estimate the rotation angle, and the fast Fourier transform to estimate global spatial shifts. Experimental results show that our proposed method can estimate the transformation parameters very accurately. Furthermore, our method performs very well in noisy environments.     

Fast medical image mixture density clustering segmentation using stratification sampling and kernel density estimation

The Gaussian mixture models (GMMs) is a flexible and powerful density clustering tool. However, the application of it to medical image segmentation faces some difficulties. First, estimation of the number of components is still an open question. Second, the speed of it for large medical image is slow. Moreover, GMMs has the problem of noise sensitivity. In this paper, the kernel density estimation method is used to estimate the number of components K, and three strategies are proposed to improve the segmentation speed of GMMs. First, a histogram stratification sampling strategy is proposed to reduce the size of the training data. Second, a binning strategy is proposed to search the neighbor points of each center data to compute the approximate density function of the samples. Third, a hill-climbing algorithm with the dynamic step size is designed to find the local maxima of the density function. The kernel density estimation method and sampling technology reduce the effect of noise. Experimental results with the simulated brain images and real CT images show that the proposed algorithm has better performance in generating explainable segmentations with faster speed than the common GMMs algorithm.     

基于白光干涉无重叠拼接的微结构表征方法

针对白光干涉技术(WLI)应用于大范围测量时图像重叠拼接会引入误差并降低测量效率这一缺欠,本文基于纳米测量机(NMM)提出了一种无重叠拼接的白光干涉测试方法。利用NMM的高精度定位能力,扩展了普通白光干涉仪的测量范围,实现了对微结构近4倍于CCD视场的大范围测量,验证了无重叠图像拼接的可实现性。