基于韦伯感知和导引滤波分层聚合快速立体图像匹配

该文利用韦伯定律和导引滤波提出基于代价分层聚合的快速立体匹配方法。首先提取立体图像对各彩色通道Weber描述符并初始化匹配代价。利用导引滤波增强匹配代价并提取视差候选;利用候选子集联合空间离散采样与自适应支持权重实现分层代价聚合;据此快速择优选取初始视差。视差求精中采用改进型双边滤波和对称映射后处理有效改善初始视差图中歧义区域。实验表明,该文方法能有效消除匹配歧义,获得分段平滑、高精度稠密视差;结构简单、快速高效且对光照变化具有鲁棒性。     

基于Tao 立体匹配框架的全局优化算法

传统的基于全局优化的立体匹配算法计算复杂度较高,在遮挡和视差不连续区域具有较差的匹配精度。提出了基于Tao 立体匹配框架的全局优化算法。首先采用高效的局部算法获取初始匹配视差;然后对得到的视差值进行可信度检测,利用可信像素点和视差平面假设使用具有鲁棒性的低复杂度算法修正不可信任像素视差值;最后改进置信度传播算法,使其能够自适应地停止收敛节点的消息传播,并对经修正的初始匹配进行优化,提高弱纹理区域匹配准确度。实验结果表明,文中算法有效地降低整体误匹配率,改善了视差不连续及遮挡区域的匹配精度;同时,降低了算法整体复杂度,兼顾了速度,具有一定的实用性。     

Onboard low-complexity compression of solar stereo images

We propose an adaptive distributed compression solution using particle filtering that tracks correlation, as well as performing disparity estimation, at the decoder side. The proposed algorithm is tested on the stereo solar images captured by the twin satellites system of NASA's Solar TErrestrial RElations Observatory (STEREO) project. Our experimental results show improved compression performance w.r.t. to a benchmark compression scheme, accurate correlation estimation by our proposed particle-based belief propagation algorithm, and significant peak signal-to-noise ratio improvement over traditional separate bit-plane decoding without dynamic correlation and disparity estimation.     

基于小基高比的快速立体匹配方法

为了提高立体匹配效率和获得高精度的亚像素级视差,该文提出一种快速的小基高比立体匹配方法。该方法首先利用积分图像加速自适应窗口和规范互相关度量的计算,然后根据可靠性约束进一步拒绝错误匹配,再采用基于迭代二倍重采样的亚像素级匹配方法为可信点计算亚像素级视差,最后利用基于图分割的视差平面拟合方法获得稠密的亚像素级视差图。实验结果表明该方法不但可获得高精度的亚像素级视差而且还提高了算法的匹配效率,满足了小基高比立体重建的需求。     

RS码与LDPC码的联合迭代译码

针对RS码与LDPC码的串行级联结构,提出了一种基于自适应置信传播(ABP)的联合迭代译码方法.译码时,LDPC码置信传播译码器输出的软信息作为RS码ABP译码器的输入;经过一定迭代译码后,RS码译码器输出的软信息又作为LDPC译码器的输入.软输入软输出的RS译码器与LDPC译码器之间经过多次信息传递,译码性能有很大提高.码长中等的LDPC码采用这种级联方案,可以有效克服短环的影响,消除错误平层.仿真结果显示:AWGN信道下这种基于ABP的RS码与LDPC码的联合迭代译码方案可以获得约0.8 dB的增益.     

Reliable fusion of ToF and stereo data based on joint depth filter

To obtain reliable depth images with high resolution, a novel method is proposed in this study that fuses data acquired from time-of-flight (ToF) and stereo cameras, through which the advantages of both active and passive sensing are utilised. Based on the classic error model of the ToF, gradient information is introduced to establish the likelihood distribution for all disparity candidates. The stereo likelihood is estimated in parallel based on a 3D adaptive support-weight approach. The two independent likelihoods are unified using a maximum likelihood estimation, a process also referred to as a joint depth filter herein. Conventional post-processing methods such as a mutual consistency check are also used after applying a joint depth filter. We also propose a novel hole-filling method based on the seed-growing algorithm to retrieve missing disparities. Experiment results show that the proposed fusion method can produce reliable high-resolution depth maps and outperforms other compared methods.     

一种快速区域立体匹配算法设计及实现

为了精确建立两幅图像像素间的对应关系,克服区域立体匹配算法计算量大的缺点,提出了一种改进的区域立体匹配算法。改进的区域匹配算法运用双向立体匹配策略来提高两幅图像像素间的匹配精度,并结合mean-shift图像分割技术,在同一区域具有相同视差的假设前提下获得图像的稠密视差;为了克服区域立体匹配计算量大的缺点,该算法引入伪极线约束将真正匹配点的位置限定在一个非常小的范围内,从而大大减少了匹配点的搜索范围,有效地降低了匹配算法的计算量。实验结果表明,改进的双向匹配算法可以有效地缩小匹配搜索范围,并实现图像间的精确稠密匹配。     

Parallel framework for dense disparity map estimation using Hamming distance

A novel framework for sparse and dense disparity estimation was designed, and the proposed framework has been implemented in CPU and GPU for a parallel processing capability. The Census transform is applied in the first stage, and then, the Hamming distance is later used as similarity measure in the stereo matching stage followed by a matching consistency check. Next, a disparity refinement is performed on the sparse disparity map via weighted median filtering and color K-means segmentation, in addition to clustered median filtering to obtain the dense disparity map. The results are compared with state-of-the-art frameworks, demonstrating this process to be competitive and robust. The quality criteria used are structural similarity index measure and percentage of bad pixels (B) for objective results and subjective perception via human visual system demonstrating better performance in maintaining fine features in disparity maps. The comparisons include processing times and running environments, to place each process into context.     

基于改进Census变换与梯度融合的立体匹配算法

双目立体匹配根据视差原理将平面视觉转化到三维立体视觉,是三维重建的核心步骤之一。针对局部立体匹配算法在深度不连续、弱纹理区域匹配精度低且易受光照、噪声等因素干扰的问题,提出了一种改进的立体匹配算法。首先,在代价计算阶段将改进的Census代价与梯度代价进行融合,并采用引导滤波算法对图像进行多尺度代价聚合;然后,采用赢家通吃算法计算初始视差;最后,采用左右一致性检测、中值滤波进行视差后处理,得到最终的视差图。实验结果表明,本算法在Middlebury2.0测试平台上的平均误匹配率为5.11%,且具有很好的稳健性和实用性。     

Bilateral exchange of soft-information for iterative reliability-based decoding with adaptive belief propagation

For linear block codes without a sparse graph representation, there exists an iterative decoding algorithm which combines the traditional reliability-based decoding (RBD) with adaptive belief propagation (ABP) to achieve a good tradeoff between the error performance and decoding complexity. However, in the original design of the iterative scheme, only a one-way flow of soft-information from the ABP-part to the RBD-part is available, hence limiting the performance of iterative processing. In this study, several low-complexity schemes are presented for the RBD-part to produce desirable soft-outputs such that decoded results can be bilaterally exchanged between both of the RBD and ABP parts. Simulation results also verify the superiority of the proposed idea over the conventional design.     

Automated Matching of Pairs of SIR-B Images for Elevation Mapping

During the SIR-B mission in October 1984, a significant number of overlapping synthetic aperture radar (SAR) images of various ground areas was collected. This has offered the first opportunity to perform stereo analyses on images from space that cover large ground areas to determine elevation information. This paper presents the preliminary results of an investigation to obtain elevation data from stereo pairs of SIR-B images. First, the accuracy with which elevation information can be derived from SIR-B image pairs is evaluated theoretically. It is shown that elevation accuracy is a function of the slant range resolution, the incidence angles with which the stereo pair is obtained, the accuracies in spacecraft state estimation, and determination of corresponding pixels in the stereo pair. Next, a hierarchical method is developed to match the corresponding pixels. This method involves iterative removal of local distortions and correlations of pairs of local neighborhoods in the two images. Since it is necessary to perform the matching at every pixel in the image, it is very computationally intensive. Therefore, it has been implemented on the Massively Parallel Processor (MPP) at the Goddard Space Flight Center (GSFC). The MPP's speed permits two iterations of this technique to operate on a pair of 512 × 512 images within 7 s. Results of applying this algorithm to SIR-B images of Mount Shasta, CA, are shown. The matching algorithm performs well in regions of the image with significant features.     

低复杂度的自适应置信差分迭代译码算法

针对中短码长的低密度奇偶校验规则码(Low Density Parity Check, LDPC)规则码,该文采用消息更新规则改进和因子图变换方法,提出一种低复杂度差分迭代译码算法。在置信传播算法的基础上,仅当变量节点的消息值振荡时引入差分映射策略,得出一种选择性的置信差分规则,自适应地调整校验节点消息的归一化系数,提高译码性能。同时,采用展开校验节点的图变换方法,将计算复杂度从随节点度分布指数性增长降至线性增长。分别在高斯白噪声信道和瑞利衰落信道下进行仿真实验,结果表明该算法和基于图变换的其他低复杂度译码算法相比,性能优越且复杂度低,和对数似然比的置信传播算法(LLR-BP)相比,高信噪比区域内的性能优异,低信噪比区域内的计算复杂度明显降低。     

结合图像梯度和亮度的并行信任传播算法

对立体匹配问题建立马尔可夫随机场模型,使用并行的多尺度信任传播算法求解马尔可夫随机场的能量最小化问题.在传统串行算法基础上利用CUDA技术实现了并行计算,并结合图像的梯度和亮度信息计算能量函数的数据项,平滑项采用两个相邻像素视差的绝对差度量.以标准的Middlebury立体数据集做为输入,实验结果表明：算法具有很好的实时性能,运行时间远小于传统的串行算法,深度图结果较优.     

基于改进Census变换的局部立体匹配算法

针对基于传统Census变换的立体匹配算法鲁棒性差和精度不高的问题,提出一种基于改进Census变换的自适应权重立体匹配算法.首先,用Census变换窗口中邻域像素的中值来替换中心像素的灰度值,克服了邻域像素对中心像素的依赖;然后用自适应权重的方法分别计算匹配代价和进行立体匹配,得到初始视差;最后通过左右一致性校验和亚像素提精的方法得到稠密的视差图.实验结果表明,该算法有很强的鲁棒性和很高的匹配精度.     

一种小波域与空域相结合的图像滤波方法

利用小波阈值去噪方法和传统空间域Lee 滤波的特点, 提出了一种图像去噪的的组合滤波方案。首先在小波域对图像阈值去噪, 得到预去噪图像; 再在空间域上利用自适应Wiener 滤波器进一步提高恢复图像的精度。为了保证小波域和空间域两种算法之间的匹配, 对预去噪图像中残留噪声的分布进行了研究, 对其噪声方差估计做了改进, 提出了一种估计噪声方差的近似最优公式。仿真实验表明, 与单独的在小波域或空域去噪相比, 该方法的均方误差和信噪比指标均得到了改善。     

Edge-preserving guided filtering based cost aggregation for stereo matching

Stereo matching has been widely used in various computer applications and it is still a challenging problem. In stereo matching, the filter-based stereo matching methods have achieved outstanding performance. A local stereo matching method based on adaptive edge-preserving guided filter is presented in this paper, which can achieve proper cost-volume filtering and keep edges well. We introduce a gradient vector of the enhanced image generated by the proposed filter into the cost computation and the Census transform is adopted in the cost measurement. This cost computation method is robust against radiometric variations and textureless areas. The edge-preserving guided filter approach is proposed to aggregate the cost volume, which further proves the effectiveness of edge-preserving filter for stereo matching. The experiments conducted on Middlebury benchmark and KITTI benchmark demonstrate that the proposed algorithm produces better results compared with other edge-aware filter-based methods.     

Stereo matching using colour consistency and hierarchical stochastic diffusion

A stereo matching algorithm is proposed using colour consistency and hierarchical stochastic diffusion. The colour consistency is introduced to the prior Markov random field (MRF) model to consider both the smoothness and the discontinuity of the disparity field. The energy function derived from the MRF model is minimised in the hierarchical stochastic diffusion, which first propagates the probability distribution of the reliable disparity field into the neighbourhood. Experimental results show good disparity maps compared with other propagation based methods.     

Local pixel statistics based impulse detection and hybrid color filtering for restoration of digital color images

This paper presents a two stage filtering system to remove random valued impulse noise from color images based on local statistics of the filtering window under consideration. In the first stage, to detect the noisy pixel, the locally adaptive threshold is derived from the pixels of the filtering window. In the second stage, the restoration of the noisy pixel is done on the basis of brightness and chromaticity information obtained from the neighbouring pixels in the filtering window. Simulation results show that the proposed scheme yields much superior performance in comparison with other color image filtering methods.     

基于引导滤波及视差图融合的立体匹配

立体匹配是双目视觉领域的重要研究方向.为在保证图片纹理区域匹配精度的同时降低弱纹理区域的误匹配率,提出一种基于引导滤波及视差图融合的立体匹配方法.首先,根据图像颜色相似性将图片划分为纹理较丰富区域和弱纹理区域.接着,分别采用不同参数的引导滤波进行代价聚合及视差计算,得到两张视差图.然后依据纹理区域划分的结果对获得的两张视差图进行融合.最后,通过左右一致性检测、加权中值滤波等视差优化步骤得到最终视差图.对Middlebury测试平台上标准图像对的实验结果表明,该方法在6组弱纹理图像上的平均误匹配率为9.67％,较传统引导滤波立体匹配算法具有更高的匹配精度.     

基于扩展相位相关的小基高比立体匹配方法

为获得高精度亚像素级视差以满足小基高比摄影测量需求,提出一种基于扩展相位相关的小基高比立体匹配方法.该方法采用分步式策略,首先利用自适应窗口匹配法计算整像素级视差,然后在整像素级视差的指导下建立同名像点之间的对应关系,再分别以左右同名像点为中心截取子图像,最后利用扩展相位相关匹配法对子图像进行亚像素级匹配,获得亚像素级视差.采用小基高比立体像对和带有亚像素级视差的模拟立体像对进行实验,结果表明基于扩展相位相关的小基高比立体匹配方法在精度和效率方面具有优越性能.