Content adaptive video denoising based on human visual perception

In this paper, we propose content adaptive denoising in highly corrupted videos based on human visual perception. We introduce the human visual perception in video denoising to achieve good performance. In general, smooth regions corrupted by noise are much more annoying to human observers than complex regions. Moreover, human eyes are more interested in complex regions with image details and more sensitive to luminance than chrominance. Based on the human visual perception, we perform perceptual video denoising to effectively preserve image details and remove annoying noise. To successfully remove noise and recover the image details, we extend nonlocal mean filtering to the spatiotemporal domain. With the guidance of content adaptive segmentation and motion detection, we conduct content adaptive filtering in the YUV color space to consider context in images and obtain perceptually pleasant results. Extensive experiments on various video sequences demonstrate that the proposed method reconstructs natural-looking results even in highly corrupted images and achieves good performance in terms of both visual quality and quantitative measures.     

Enhancing dynamic videos for surveillance and robotic applications: The robust bilateral and temporal filter

Over the last few decades, surveillance applications have been an extremely useful tool to prevent dangerous situations and to identify abnormal activities. Although, the majority of surveillance videos are often subjected to different noises that corrupt structured patterns and fine edges. This makes the image processing methods even more difficult, for instance, object detection, motion segmentation, tracking, identification and recognition of humans.This paper proposes a novel filtering technique named robust bilateral and temporal (RBLT), which resorts to a spatial and temporal evolution of sequences to conduct the filtering process while preserving relevant image information. A pixel value is estimated using a robust combination of spatial characteristics of the pixel's neighborhood and its own temporal evolution. Thus, robust statics concepts and temporal correlation between consecutive images are incorporated together which results in a reliable and configurable filter formulation that makes it possible to reconstruct highly dynamic and degraded image sequences.The filtering is evaluated using qualitative judgments and several assessment metrics, for different Gaussian and Salt–Pepper noise conditions. Extensive experiments considering videos obtained by stationary and non-stationary cameras prove that the proposed technique achieves a good perceptual quality of filtering sequences corrupted with a strong noise component.     

基于改进双边滤波的多尺度运动目标检测方法

提出一种基于改进双边滤波的运动多尺度目标检测方法,以提高对弱小目标的检测能力.首先对视频或序列红外图像进行改进双边滤波处理,提高目标的对比度,同时抑制背景的边缘噪声及随机噪声.然后对目标进行三维匹配滤波,获得若干组速度匹配叠加强度图像.最后,在这些图像中进行基于NNLoG(归一化负LoG算子)的多尺度目标检测,得到序列图像或视频段的最佳匹配速度及增强后的图像.可最终计算出目标在序列图像或视频中的运动方程.通过大量的实验及对比实验可知,改进双边滤波、三维匹配滤波及NNLoG算子综合处理效果都较好,可有效检测序列图像或视频中的目标.     

基于加权平均值的汽车车牌监控图像过滤技术研究

车牌监控图像由于照明、天气、运动目标位置和运动目标速度的不同,图像质量差异很大,从而不利于车牌监控的定位和识别。本文采用最大值法、平均值法和加权平均值法三种方法对车牌监控图像进行过滤,结果表明：采用加权平均值法进行车牌图像颜色过滤能够保留绝大部分的汽车车牌信息,使得目标和背景之间边界清晰,是一种较好的车牌图像彩色过滤方法。     

双域滤波三元组度量学习的行人再识别

在图像的捕获、传输或者处理过程中都有可能产生噪声,当图像被大量噪声影响时,许多行人再识别(ReID)方法将很难提取具有足够表达能力的行人特征,表现出较差的鲁棒性。该文主要针对低质图像的行人再识别问题,提出双域滤波分解构建3元组,用于训练度量学习模型。所提方法主要分为两个部分,首先分析了监控视频中不同图像噪声的分布特性,通过双域滤波进行图像增强。然后基于双域滤波分解对图像噪声具有很好的分离作用,该文提出一种新的3元组构建方式。在训练阶段,将双域滤波生成的低频原始图像和高频噪声图像,与原图一起作为输入3元组,网络可以进一步抑制噪声分量。同时优化了损失函数,将3元组损失和对比损失组合使用。最后利用re-ranking扩充排序表,提高识别的准确率。在加噪Market-1501和CUHK03数据集上的平均Rank-1为78.3%和21.7%,平均准确率均值(mAP)为66.9%和20.5%。加噪前后的Rank-1精度损失只有1.9%和7.8%,表明该文模型在含噪情况表现出较强的鲁棒性。     

基于视觉效应的视频信息电磁泄漏抑制方法研究

计算机的电磁辐射会包含视频信息从而造成的信息泄漏,本文在随机置乱的基础上,根据人眼视觉效应提出了互补置乱的方法来抑制视频信息通过电磁辐射的泄漏.通过对相邻的每帧视频信息进行加减随机噪声,使得人眼视觉观察效果抵消掉噪声对视频图像的干扰,在叠加噪声的同时,保证了视频图像的清晰度,同样也达到了抑制视频信息电磁泄漏的功能.最后也通过实际截获实验,验证了该方法的可行性.     

基于低秩分解和改进的非局部平均的SAR图像相干斑抑制

为抑制合成孔径雷达（SAR）图像成像过程中形成的相干斑噪声,提出了一种基于低秩分解和改进的非局部平均的SAR图像相干斑去噪方法。首先将SAR图像进行对数处理,将乘性噪声转换为加性噪声;然后利用低秩稀疏分解将对数图像分解成低秩图像部分和稀疏图像部分;接着对含噪严重的稀疏图像部分分析其结构张量,生成非局部平均滤波所需的衰减因子,进行改进的非局部平均滤波去噪;最后再做图像合成,经指数变换得到去噪后的SAR图像。实验结果表明,该方法经视觉评价、边缘保持指数（EPI）和等效视数（ENL）等方面评测,具有较好的抑制噪声和保持边缘及纹理细节的能力。      

Detecting moving object from dynamic background video sequences via simulating heat conduction

Moving object detection is one of the essential tasks for surveillance video analysis. The dynamic background often composed by waving trees, rippling water or fountains, etc. in nature scene greatly interferes with the detection of moving objects in the form of noise. In this paper, a method simulating heat conduction is proposed to extract moving objects from dynamic background video sequences. Based on the visual background extractor (ViBe) with an adaptable distance threshold, we design a temperature field relying on the generated mask image to distinguish between the moving objects and the noise caused by dynamic background. In temperature field, a brighter pixel is associated with more energy. It will transfer a certain amount of energy to its neighboring darker pixels. Through multiple steps of energy transfer the noise regions loss more energy so that they become darker than the detected moving objects. After heat conduction, K-Means algorithm with the customized initial clustering centers is utilized to separate the moving objects from background. We test our method on many videos with dynamic background from public datasets. The results show that the proposed method is feasible and effective for moving object detection from dynamic background sequences.     

Background foreground boundary aware efficient motion search for surveillance videos

The huge amount of data in surveillance video coding demands high compression rates with lower computational requirements for efficient storage and archival. The motion estimation is a very time-consuming process in the traditional video coding framework, and hence reducing computational complexity is a pressing task, especially for surveillance videos. The presence of significant background proportion in surveillance videos makes its special case for coding. The existing surveillance video coding methods propose separate search mechanisms for background and foreground regions. However, they still suffer from misclassification and inefficient search strategies since it does not consider the inherent motion characteristics of the foreground regions. In this paper, a background-foreground-boundary aware block matching algorithm is proposed to exploit special characteristics of the surveillance videos. A novel three-step framework is proposed for boundary aware block matching process. For this, firstly, the blocks are categorized into three classes, namely, background, foreground, and boundary blocks. Secondly, the motion search is performed by employing different search strategies for each class. The zero-motion vector-based search is employed for background blocks. Whereas, to exploit fast and directional motion characteristics of the boundary and foreground blocks, the eight rotating uni-wing diamond search patterns are proposed. Thirdly, the speed-up is achieved through the novel region-based sub-sampled structure. The experimental results demonstrate that two to four times speed-up over existing methods can be achieved through this scheme while maintaining better matching accuracy.     

一种改进型图像降噪方法

针对图像获取中易受到噪声干扰的问题,介绍了图像去噪处理的几种常用算法（邻域平均法、中值滤波法、图像间的平均滤波）,对比阐述了各自的优缺点及适用范围。提出了一种基于平滑滤波的小波阈值图像去噪算法,该算法采用中值滤波和小波阈值相结合的方式对图像进行平滑处理。实验结果表明,该方法不仅有利于图像噪声的去除,而且边缘信息也得到了较好的保留,使图像具有更好的视觉效果,还原出图像的本来面目。     

Adaptive progressive filter to remove impulse noise in highly corrupted color images

In this paper, an adaptive progressive filtering (APF) technique with low computational complexity is proposed for removing impulse noise in highly corrupted color images. Color images that are corrupted with impulse noise are generally filtered by applying a vector-based approach. Vector-based methods tend to cluster the noise and receive a lower noise reduction performance when the noise ratio is high. To improve the performance, in the proposed technique, a new reliable estimation of impulse noise intensity and noise type is made initially, and then a progressive restoration mechanism is devised, using multi-pass non-linear operations with selected processing windows adapted to the estimation. The effect of impulse detection based on geometric characteristics and features of the corrupt pixel/pixel regions and the exact estimation of impulse noise intensity and type are used in the APF to efficiently support the progressive filtering mechanism. Through experiments conducted using a range of color images, the proposed filtering technique has demonstrated superior performance to that of well-known benchmark techniques, in terms of standard objective measurements, visual image quality, and the computational complexity.     

Recognition of dynamic video contents with global probabilistic models of visual motion.

The exploitation of video data requires methods able to extract high-level information from the images. Video summarization, video retrieval, or video surveillance are examples of applications. In this paper, we tackle the challenging problem of recognizing dynamic video contents from low-level motion features. We adopt a statistical approach involving modeling, (supervised) learning, and classification issues. Because of the diversity of video content (even for a given class of events), we have to design appropriate models of visual motion and learn them from videos. We have defined original parsimonious global probabilistic motion models, both for the dominant image motion (assumed to be due to the camera motion) and the residual image motion (related to scene motion). Motion measurements include affine motion models to capture the camera motion and low-level local motion features to account for scene motion. Motion learning and recognition are solved using maximum likelihood criteria. To validate the interest of the proposed motion modeling and recognition framework, we report dynamic content recognition results on sports videos.     

Multi-video synopsis for video representation

The world is covered with millions of cameras with each recording a huge amount of video. It is a time-consuming task to watch these videos, as most of them are of little interest due to the lack of activity. Video representation is thus an important technology to tackle with this issue. However, conventional video representation methods mainly focus on a single video, aiming at reducing the spatiotemporal redundancy as much as possible. In contrast, this paper describes a novel approach to present the dynamics of multiple videos simultaneously, aiming at a less intrusive viewing experience. Given a main video and multiple supplementary videos, the proposed approach automatically constructs a synthesized multi-video synopsis by integrating the supplementary videos into the most suitable spatiotemporal portions within this main video. The problem of finding suitable integration between the main video and supplementary videos is formulated as the maximum a posterior (MAP) problem, in which the desired properties related to a less intrusive viewing experience, i.e., informativeness, consistency, visual naturalness, and stability, are maximized. This problem is solved by using an efficient Viterbi beam search algorithm. Furthermore, an informative blending algorithm that naturalizes the connecting boundary between different videos is proposed.The proposed method has a wide variety of applications such as visual information representation, surveillance video browsing, video summarization, and video advertising. The effectiveness of multi-video synopsis is demonstrated in extensive experiments over different types of videos with different synopsis cases.     

一种基于斑点抑制的SAR图像舰船航迹检测算法

该文分析了合成孔径雷达(SAR)图像中舰船航迹的特性和斑点噪声模型及其局部统计特性。在此基础上,提出了一种先基于小波变换进行斑点噪声抑制,再基于Radon变换进行航迹检测的方法。对数据处理的结果表明,该方法较直接对SAR图像应用Radon变换能更有效、准确地检测到SAR图像中的舰船航迹。     

The Use of Vanishing Point for the Classification of Reflections From Foreground Mask in Videos

Extraction of foreground is a basic task in surveillance video analysis. In most real cases, its performance is heavily based on the efficiency of shadow detection and on the analysis of lighting conditions and reflections caused by mirrors or other reflective surfaces. This correspondence is focused on the improvement of foreground extraction in the case of planar reflective surfaces. We show that the geometric model of a scene with a planar reflective surface is reduced to the estimation of vanishing-point for the case of an auto-epipolar (skew-symmetric) fundamental matrix. The correspondences for the vanishing-point estimation are extracted from motion statistics. The knowledge of the position of the vanishing point allows us to integrate the geometric model and the motion statistics into image foreground-extraction to separate foreground from reflections, and thus to achieve better performance. The experiments confirm the accuracy of the vanishing point and the improvement of the foreground image mask by removing reflected object parts.      

基于噪声检测的高密度椒盐噪声自适应滤波算法

为了减少图像中的椒盐噪声对后续图像处理的影响,针对高密度噪声污染图像,提出了基于噪声检测的高密度椒盐噪声滤波算法。噪声检测方法理论可靠,保证了较高的噪声检测率,根据噪声点邻域信号点分布的不同采用不同的策略,能最大限度的保护图像的细节信息,使得高密度噪声污染图像也能得到较好地恢复。实验结果表明,所提出的滤波算法具有较强的自适应性、较高的算法保真率及较好的滤波效果。     

基于CxImage库的SAR图像Speckle噪声抑制

合成孔径雷达图像的相干斑噪声抑制是SAR信息处理中的一个重要环节。经典的Speckle噪声抑制通常作为一个处理模块集成在软件中,但现有软件代码封装无法二次开发。针对文中提出了一种基于开源图像库CxImage的空间域自适应Speckle噪声抑制算法的应用与集成。将CxImage图像库链接入MFC应用程序框架中,利用其图像管理、维护、处理功能对SAR图像进行维护管理,集成多种经典的空间域自适应滤波方法,并以ERS-2卫星的PRI SAR数据为例,进行Speckle噪声滤波处理,选取适当的滤波效果评价参数,对滤波结果进行比较,最终得出各滤波算法应用于SAR图像滤波的优劣。     

基于自适应阈值的前景提取算法

针对固定场景视频监控中运动目标提取的问题,提出了一种基于自适应阈值的前景提取方法。该算法通过混合高斯模型(GMM)对背景建模及更新,利用自适应阈值的方法,实现了模型门限的自适应调整和前景目标的分割。然后通过阴影抑制,滤波以及形态学处理的方法对前景目标进行后处理,改善了前景目标分割的质量。通过对不同场景的测试仿真表明,该算法能够有效地并且比较完整地提取出运动目标。     

一种软件实现虚拟景深的方法

影视后期制作或图像合成中，动态实时改变显示画面的景深能进一步增强画面的真实感，但由于实时性要求较高，软件实现在处理速度方面容易产生瓶颈。为此，采用预处理的办法，分别对前景和背景进行低通滤波，得到前、背景各自的模拟帧序列，通过调节前景和背景帧序列的位置，模拟确定其各自的变焦程度，然后将前景和背景动态合成具有虚拟景深效果的画面。     

基于生成式对抗网络的太赫兹图像增强

太赫兹扫描成像中,由于激光器功率波动和仪器振动等原因,导致图像对比度较低,成像质量有待提高,且目前针对太赫兹图像的处理还停留在传统算法阶段.本文结合深度学习思想,提出了一种基于生成式对抗网络的图像增强方法.通过对训练集图像引入模糊和噪声,学习低质量图像和高质量图像之间的映射关系,并将其应用在真实太赫兹图像中.实验结果表...