Stitched image quality assessment based on local measurement errors and global statistical properties

Image stitching is developed to generate wide-field images or panoramic images for virtual reality applications. However, the quality assessment of stitched images with respect to various stitching algorithms has been less studied. Effective stitched image quality assessment (SIQA) is advantageous to evaluate the performance of various stitching methods and optimize the design of stitching methods. In this paper, we propose a novel SIQA method by exploiting local measurement errors and global statistical properties for feature extraction. Comprehensive image attributes including ghosting, misalignment, structural distortion, geometric error, chromatic aberrations and blur are considered either locally or globally. The extracted local and global features are aggregated into an overall quality via regression. Experimental results on two benchmark databases demonstrate the superiority of the proposed metric over both the state-of-the-art quality models designed for natural images and stitched images.     

Quality assessment for color correction-based stitched images via bi-directional matching

With the deepening of social information, the panoramic image has drawn a significant interest of viewers and researchers as it can provide a very wide field of view (FoV). Since panoramic images are usually obtained by capturing images with the overlapping regions and then stitching them together, image stitching plays an important role in generating panoramic images. In order to effectively evaluate the quality of stitched images, a novel quality assessment method based on bi-directional matching is proposed for stitched images. Specifically, dense correspondences between the testing and benchmark stitched images are first established by bi-directional SIFT-flow matching. Then, color-aware, geometric-aware and structure-aware features are respectively extracted and fused via support vector regression (SVR) to obtain the final quality score. Experiments on our newly constructed database and ISIQA database demonstrate that the proposed method can achieve comparable performance compared with the conventional blind quality metrics and the quality metrics specially designed for stitched images.     

基于HSI梯度统计特性的图像修复算法

为更好保持修复后图像的结构连贯性及与邻域信 息的连续一致性,提出了基于HSI梯度统计特性的图像修复算法。 该方法首先在HSI色彩空间提取图像的梯度特征,利用梯度均值和方差等统计量重新定义样 本块的结构相似性,在此基 础上构造加权的数据项和平滑项的全局能量约束方程,同时自适应地确定计算全局能量的样 本块尺寸和多分辨率层数,并 利用图割算法进行最优化求解。由于HSI色彩空间能更加充分地提取图像的梯度特征,利用 样本块HSI梯度统计特性使 得搜寻得到的匹配标签更加合理。实验结果表明,该算法对于自然图像中小区域破损和目标 物体移除方面有较好的应用, 修复结果很好地保持了图像结构的连贯性,并削弱了接缝效应,更符合人眼视觉要求。     

基于高阶奇异值分解和深度学习的光场图像拼接方法

光场图像拼接旨在提高光场图像的视场角。考虑到光场数据包含较多冗余,且传统拼接方法对于低纹理场景的光场图像鲁棒性不足,本文提出一种基于高阶奇异值分解(high-order singular value decomposition, HOSVD)和深度学习的光场图像拼接方法。首先,通过光流估计和HOSVD对光场图像进行降维,得到所有视角下一致空间信息的主基和不同视角下高频信息的其他基带。其次,提出注意力增强的无监督单应性估计网络来提高图像的配准精度。最后,将扭曲后的参考基带和目标基带进行光场重建与图像融合,得到最终的拼接光场。实验结果表明,该方法在拼接光场的主客观质量和角度一致性方面表现出较好的性能。     

航空面阵图像拼接的累积误差消除方法

针对航空面阵遥感图像拼接算法一般存在误差累积的问题,提出了航空面阵图像拼接的累积误差消除方法。首先,利用航空遥感平台位置信息与姿态信息完成每幅图像投影变换与粗拼接,确保了每幅投影变换后图像拼接误差的独立性,从而避免了误差累积现象;然后,采用SIFT算法提取粗拼接相邻图像重叠区域的同名像点对,并根据同名像点对的位置差异,按照误差均分的原则,逐张独立地进行坐标微调及坐标精调,直到满足一定精度为止,以提高图像拼接精度,同时避免坐标调整时产生的误差累积。最后,通过主观评价（对比校正后卫星图像和文中拼接效果图像的地理对应关系）和客观评价（SSIM算法）对图像拼接质量进行评价。理论分析与实验结果表明:该方法能有效规避图像拼接中的误差累积问题,相对当前主要拼接方法,能够很好地与地理空间二维坐标保持一致性,且具有很好的实用价值和应用前景。     

基于稀疏特征匹配和形变传播的无缝图像拼接

该文给出了一种基于稀疏特征匹配和形变传播的无缝图像拼接方法。首先,在配准图像的重叠区域中寻找一条结构误差最小的最佳接缝,从一边的图像中选取目标区域;接着,沿着接缝在两边的图像区域中检测出显著的结构特征,并进行特征匹配,获得目标区域中接缝上的匹配特征点及与之关联的边缘特征点的结构形变矢量;然后,通过求解泊松方程,将这些稀疏的形变矢量稳定和平滑地传播到目标区域内部,得到目标区域中各点的形变矢量;最后,由形变矢量通过内插获得目标区域的梯度场,并由梯度场重构出最终结果。该方法执行方便快速,不需要复杂的特征检测,能够统一地纠正图像拼接中较大的结构错位和颜色过渡不自然,在全局上消除结构接缝和颜色接缝。与其它方法比较,该方法获得较明显的改进。     

视频图像拼接和应用

论述实时处理由若干个摄像头采集的图像，即利用视频图像边界重叠的部分进行拼接，形成全景式的视频，然后实时显示拼接后的视频图像。     

小波域结构相似度融合图像质量评价方法

针对融合图像质量评价问题,分析了图像质量评价与融合图像质量评价的关系,给出了融合图像质量评价方法的一般表达公式,指出构造实际并不存在的参考图像是解决融合图像质量评价问题的关键。在此基础上,基于空域结构相似度评价方法,对输入源图像和融合图像分别进行小波分解,利用输入源图像小波分解系数构造参考图像小波系数,然后根据人眼视觉敏感度带通特性对参考图像和融合图像的各小波频带进行加权,从而得到整幅图像的小波域结构相似度评价指标,利用目标可探测性、细节可分辨能力和图像整体舒适性构成主观评价指标分别和交互信息量、基于空域的结构相似度比较。实验结果表明,相比于传统的客观评价方法,提出的方法所得结果与主观评价结果的一致性更好。     

非一致重叠率大批量航拍远红外图像拼接方法研究

由于远红外图像存在分辨率低、具有大量重复结构和稀疏结构等问题,大批量远红外图像的拼接工作面临很多挑战。文中针对航空场景下的远红外图像拼接,将图像对齐过程分为顺序图像序列对齐和多列图像对齐。单列图像基于非极大值抑制求取单应性矩阵,并推导传递关系进行拼接。列间图像通过划分网格,结合区域相似变换和局部单应变换逐网格优化变换矩阵的权重,并通过网格变换的递推实现拼接。文中所述的拼接方法能够避免连续多张远红外图像拼接时导致的特征消失问题,同时适应多列图像拼接时上下重叠率不一致的问题,最终实现了大批量远红外图像的拼接。     

Image quality assessment based on adaptive multiple Skyline query

Non-reference image quality assessment has attracted great emphasis in recent years. Traditional image quality assessment algorithms based on structural similarity cannot make full use of the image gradient features, and the contrast similarity features often ignore the consistency of continuous color blocks within the image, which leads to large discrepancy between the evaluation result and the subjective judgment of human vision system. In this paper, we propose a deep model for image quality assessment where the spatial and visual features of image are both considered. For better feature fusion, we design an adaptive multiple Skyline query algorithm named MSFF, which takes as input multiple features of images, and learns the feature weights through end-to-end training. Extensive experiments on image quality assessment tasks prove that the proposed model exhibits superior performance compared with existing solutions.     

Image quality assessment based on gradient similarity

In this paper, we propose a new image quality assessment (IQA) scheme, with emphasis on gradient similarity. Gradients convey important visual information and are crucial to scene understanding. Using such information, structural and contrast changes can be effectively captured. Therefore, we use the gradient similarity to measure the change in contrast and structure in images. Apart from the structural/contrast changes, image quality is also affected by luminance changes, which must be also accounted for complete and more robust IQA. Hence, the proposed scheme considers both luminance and contrast-structural changes to effectively assess image quality. Furthermore, the proposed scheme is designed to follow the masking effect and visibility threshold more closely, i.e., the case when both masked and masking signals are small is more effectively tackled by the proposed scheme. Finally, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to obtain the overall image quality score. Extensive experiments conducted with six publicly available subject-rated databases (comprising of diverse images and distortion types) have confirmed the effectiveness, robustness, and efficiency of the proposed scheme in comparison with the relevant state-of-the-art schemes.     

Fast and robust seam estimation to seamless image stitching

Image stitching has a wide range of applications in computer vision/graphics and virtual reality. Seam estimation is one of the key steps in image stitching. This step can relieve ghosts and artifacts that were generated by misalignment or moving objects in the overlap region. This paper presents a fast and robust seam estimation method (FARSE) by defining gray-weighted distance and gradient-domain region of differences to avoid visible seams and ghosting. The optimal seam is estimated by searching in two weighted matrices, namely cost matrix and value matrix. The proposed method could be simply implemented. Results indicate that the FARSE method is scale-invariant and it is fast and more robust than the other methods.     

改进最佳缝合线的红外图像拼接方法

变电站场景的红外图像受噪声干扰大、纹理信息不明显,拼接过程中易出现拼接痕迹或重影现象。针对上述问题,本文提出一种改进最佳缝合线的红外图像拼接方法。该方法首先采用SIFT算法提取图像区域特征,实现图像配准;然后在两幅图像的重合区域上引入局部权重系数,并对图像颜色差异强度进行形态学操作,减少红外图像的噪声干扰,以此改善能量函数图的纹理信息。最后,利用动态规划改进缝合线搜索准则,在图像重叠区域搜索出最佳缝合线。实验结果表明,与渐入渐出法和最佳缝合线法比较,本文方法在拼接后图像的平均梯度、图像清晰度和图像边缘强度均有所提高,融合区域过渡更平滑自然,拼接痕迹明显减少。     

基于多分辨率图像融合算法的岩心图像拼接

针对岩心图像由于光照不均,及其自身因素导致的拼接图像出现明显拼接缝的问题,对比分析了多种图像融合算法,并将多分辨率图像融合算法引入到岩心图像拼接中,该算法利用拉普拉斯金字塔结构将图像按频率进行分解,对各个频率上的带通图像分别进行融合,再利用金字塔反变换还原出融合结果图,最终实现了岩心图像无缝拼接。     

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。     

无拼接盲区的图像拼接光学布局及系统设计

现有的图像拼接系统具有拼接盲区的缺陷,对于监控系统会造成观察盲区,形成信息缺失,对监控安全造成威胁。本文为了消除图像拼接系统的拼接盲区,提出了一种新的无拼接盲区的图像拼接布局型式。首先,对具有拼接盲区的图像拼接布局型式与无拼接盲区的图像拼接布局型式的优缺点进行分析;其次,以3个非制冷长波红外摄像机图像拼接系统为例,对非制冷长波红外摄像机镜头进行设计分析;最后,为了得到比较好的拼接图像,对待拼接的红外摄像机的畸变提出相应的处理措施。     

A variational method for multiple-image blending

The main aim of this paper is to develop an algorithm for blending of multiple images in the image-stitching process. Our idea is to propose a variational method containing an energy functional to determine both a stitched image and weighting mask functions of multiple input images for image blending. The existence of the solution of the proposed energy functional is shown. We also present an alternative minimizing algorithm to solve the proposed model numerically and show the convergence of this algorithm. Experimental results show that the proposed model works effectively and efficiently and that the proposed method is competitive with the tested existing methods under noisy conditions.     

Quality adaptive least squares trained filters for video compression artifacts removal using a no-reference block visibility metric

Compression artifacts removal is a challenging problem because videos can be compressed at different qualities. In this paper, a least squares approach that is self-adaptive to the visual quality of the input sequence is proposed. For compression artifacts, the visual quality of an image is measured by a no-reference block visibility metric. According to the blockiness visibility of an input image, an appropriate set of filter coefficients that are trained beforehand is selected for optimally removing coding artifacts and reconstructing object details. The performance of the proposed algorithm is evaluated on a variety of sequences compressed at different qualities in comparison to several other de-blocking techniques. The proposed method outperforms the others significantly both objectively and subjectively.     

A survey on analysis and implementation of state-of-the-art haze removal techniques

Haze is a poor-quality state described by the opalescent appearance of the atmosphere which reduces the visibility. It is caused by high concentrations of atmospheric air pollutants, such as dust, smoke and other particles that scatter and absorb sunlight. The poor visibility can result in the failure of multiple computer vision applications such as smart transport systems, image processing, object detection, surveillance etc. One of the major issues in the field of image processing is the restoration of images that are corrupted due to different degradations. Typically, the images or videos captured in the outside environment have low contrast, colour fade and restricted visibility due to suspended particles of the atmosphere that directly influence the image quality. This can cause difficulty in identifying the objects in the captured hazy images or frames. To address this problem, several image dehazing techniques have been developed in the literature, each of which has its own advantages and limitations, but effective image restoration remains a challenging task. In recent times, various learning (Machine learning & Deep learning) based methods greatly condensed the drawbacks of manual design of haze related features and reduces the difficulty in efficient restoration of images with less computational time and cost. The current state-of-the-art methods for haze free images, mainly from the last decade, are thoroughly examined in this survey. Moreover, this paper systematically summarizes the hardware implementations of various haze removal methods in real time. It is with the hope that this current survey acts as a reference for researchers in this scientific area and to provide a direction for future improvements based on current achievements.     

Ghost cancellation algorithms for MRI images

Ghost cancellation algorithms for restoring magnetic resonance imaging (MRI) images in medical applications are described. The approach is to model the effect of the time delays and the asymmetry of the sinusoidal readout gradient for even and odd lines with two phase functions relating the actual object density to even and odd parts of the observed image. A priori information about the phase functions is exploited in order to estimate the true object density. Examples of applying this ghost cancellation approach to liver and heart images is presented.