基于非局部均值滤波的视频图像去雨方法

提出了一种有效的基于非局部均值滤波的去雨方法。首先利用雨的空间特征检测出视频帧中的雨线区域,并对其进行标记。然后针对当前帧的被标记的像素块使用非雨块匹配算法,用于相邻的帧之间以找出和有雨像素区域相似的块。最后使用时空非局部均值滤波对块中的雨区进行重构,达到去除雨滴的目的。实验结果表明,比起常规算法,文中提出的算法可以更有效地去除雨线,恢复出更高质量的图像。     

雨滴成像中的散焦模糊复原方法及应用

针对自主研制的降水微物理特征测量仪存在的成像质量问题,重点考虑与光源相关的散焦模糊,提出了基于点扩散函数的雨滴图像复原方法,利用圆盘函数对雨滴图像进行复原,消除噪声和模糊,得到具有清晰轮廓的雨滴图像。利用与雨滴光学性质相近的玻璃球进行了定标实验,确定了成像系统散焦模糊的半径和图像二值化的阈值。外场实测结果表明,图像复原可以有效修正雨滴尺度及其谱分布,测量得到的雨滴谱和降雨强度与激光雨滴谱仪的结果具有很好的一致性。该方法应用于降水微物理特征测量仪,可以显著提高雨滴谱及其他微物理特征的测量性能。     

基于摄像视频分析的降雨量测量方法

提出一种新颖的基于视频的降雨量测量方法。首先 根据雨图亮度变化特征检测雨滴;其次,为解决雨滴离摄像机距离信息未知情况下的雨滴尺 寸估计问题,在雨滴成像光度模型中融入散聚焦因素, 构建了基于亮度差和色彩张量响应特征的散聚焦雨滴判别方法;然后,根据透视成像关系计 算聚焦雨滴的尺寸并根据气象学 雨滴谱的定义来构建观测雨滴谱;最后,为抑制观测雨滴谱中的噪声,采用气象学Gamma模 型拟合观测雨滴谱,并由模型参 数计算降雨量。在不同程度的降雨环境下实验检验算法,并与雨量计记 录数据对比,测量结果较为一致。     

基于深度学习的远程视频监控异常图像检测

针对复杂场景下远程视频监控图像异常检测困难、传统算法功能单一(仅针对某种特定场景或某种异常图像进行检测)等问题,提出一种基于深度学习的全自动远程视频异常图像检测方法。首先采用Xavier方法对自行设计的卷积神经网络(Convolutional Neural Network,CNN)的参数进行初始化,然后将标准化后的视频差分图送入CNN的输入层,通过特征提取及下采样,最后在CNN的输出层获得远程视频异常图像检测结果。实验结果表明,该方法可以对远程视频监控中突然出现遮挡、模糊和场景切换等多种异常同时进行实时在线检测,准确率可达88.75%。     

雨滴谱模型对雨衰减计算的适用性分析

雨滴谱分布模型是影响红外激光在雨中传输性能的一个重要因素。尽管现有的雨滴谱模型很多,但并未形成统一的结论,具体应用中模型的选择没有依据,使得计算中模型的选择具有一定的盲目性。针对此问题,论文从Mie散射理论出发,采用理论分析与仿真分析相结合的方法,讨论了几种雨滴的谱分布特性,比较了几种典型雨滴谱模型下的光传输衰减特性,分析了雨滴尺寸分布在计算大气衰减时的差异。并通过与实测雨衰减数据进行比较,最终得到了L-P模型对大雨条件下的衰减计算较为准确,Joss模型对小雨条件下的衰减计算较为准确的结论。论文的结论对于无线光通信等应用领域中模型的合理选择具有一定的指导意义。     

532 nm激光在雨中传输衰减模型的改进及实验

目前国内外研究激光在雨中传输的衰减问题时主要有两种模型:散射模型和遮挡模型。但是遮挡模型在处理位于激光器视场近端的大尺寸雨滴时存在理论缺陷,而散射模型对于视场远端的雨滴多次散射问题的计算量过于庞大。提出了一种基于接收端雨滴投影圆面积判据的雨滴衰减模型改进算法,对接收透镜视角内出现单个雨滴全遮挡的情况进行了修正。并且采用MATLAB对模型进行了仿真分析,得到改进的衰减模型与散射模型和遮挡模型的衰减曲线对比图。最后,通过自然雨场实验验证改进理论算法的精确度,结果表明:传输距离在100 m且降雨量为1.2 mm/h时,改进的理论模型相比较原遮挡模型精度提高了54.6%,说明上述改进算法能够提高雨滴衰减模型的精确性。该项目的研究为蓝绿激光大气通信探测技术的发展提供了一定的理论支撑。     

基于视频图像对比度的团雾检测算法

提出了一种基于视频图像对比度的团雾检测算法。首先,利用二维多点各向异性高斯滤波器检测视频图像中边缘线的位置和方向。然后,利用本文提出的彩色图像对比度模型计算每幅视频图像边缘线区域的对比度值。最后,利用支撑向量机区分有雾视频图像所具有的不同对比度值,实现团雾浓度的检测。实验结果表明,该算法对复杂场景下的团雾检测效果优于传统的方法,是一种有效的团雾检测方法。     

基于3D OGHM的视频运动目标检测算法

本文提出了一种新的视频运动目标检测算法.该算法联合了视频序列图像特有的时、空特性,采用3D Gassian-Hermite正交矩法(3D OGHM,3D Orthogonal Gassian-Hermite Moments)进行视频序列中的运动目标检测.并对提出的算法进行了深入的分析和优化,给出了3D OGHM算法的具体实现步骤.实验结果表明:算法能够从复杂场景的视频序列中有效地检测出运动目标;与同类算法相比,不仅有更好的抗噪性能,而且能更有效地增强运动目标信息,且具有较强的鲁棒性和实时性.     

广州雨滴尺寸分布

本文主要介绍利用激光雨滴谱仪在广州开展的雨滴尺寸分布测量结果。按照降雨率的数值大小，将雨分为暴雨、广延雨和小雨三类，分别给出所属雨类的雨滴尺寸的实测分布和拟合分曲线，并将拟合分布曲线和Ｌ－Ｐ、Ｍ－Ｐ，Ｊｏｓｓ分布曲线进行了对比分析。     

暗原色先验图像去雾算法研究

为了减小有雾天气对户外成像系统尤其是航拍视觉系统成像的影响,提出了一种新颖快速的基于暗原色先验和雾天图像退化模型的图像去雾方法.结合航拍图像特点,对原暗原色先验去雾箅法做了优化和改进.选取一系列户外带雾图像进行大量实验,结果表明,该方法能从物理特性上快速明显的去除雾的干扰,提高图像的清晰度,增强图像色彩和细节,复原得到...     

利用雨滴尺寸分布数据确定雷达测雨参数

本文根据我国青岛、广州和新乡地区实测雨滴尺寸分布数据,计算了球形雨滴雷达反射因子Z,椭球形雨滴雷达反射因子Z_H和差分反射率Z_DR,回归给出了这些地区的常规气象雷达测雨算式Z~R关系和多参数雷达测雨算式Z_H、Z_DR~R关系,并对其测雨精度和地区差异进行了比较.     

Visual Quality Assessment of Video and Image Sequences—A Human-based Approach

Most of the current quality assessment techniques interpret an image quality as a measure of its fidelity with another reference image, assuming the availability of that “perfect” image. It has been the concern of many researchers around the world to algorithmically assess the quality of image sequences based on human visual perception. This paper presents a novel technique for quantitatively assessing the quality of image sequences without the need for a reference image and in a way that precisely correlates to human judgement on quality. This research is a part of a larger framework that incorporates multi-objective optimisation algorithms to optimise the quality metrics of compressed videos acquired by autonomous vehicles and transmitted over low-bandwidth communication channels. Our system was trained on a dataset that involved 700 videos of 5 different categories. We validate the performance of our model and show that it highly correlates to the human subjective quality assessment.     

Spatially adaptive block-based super-resolution

Super-resolution technology provides an effective way to increase image resolution by incorporating additional information from successive input images or training samples. Various super-resolution algorithms have been proposed based on different assumptions, and their relative performances can differ in regions of different characteristics within a single image. Based on this observation, an adaptive algorithm is proposed in this paper to integrate a higher level image classification task and a lower level super-resolution process, in which we incorporate reconstruction-based super-resolution algorithms, single-image enhancement, and image/video classification into a single comprehensive framework. The target high-resolution image plane is divided into adaptive-sized blocks, and different suitable super-resolution algorithms are automatically selected for the blocks. Then, a deblocking process is applied to reduce block edge artifacts. A new benchmark is also utilized to measure the performance of super-resolution algorithms. Experimental results with real-life videos indicate encouraging improvements with our method.     

联合运动估计与基于模式的超分辨率图像重构

超分辨率图像重构是利用关于同一场景的多帧低分辨率图像重构出一幅具有更高分辨率图像的过程.已有的超分辨率图像重构算法对于人工模拟所得到的低分辨率图像序列具有很好的效果,但对于拍摄到的真实低分辨率图像序列而言,重构后的图像往往比较模糊,有时甚至仍然无法分辨.为此,本文提出了一个联合运动估计与基于模式的超分辨率图像重构算法.实验结果表明,该算法能够得到优于常规算法的高分辨率图像.     

An information fidelity criterion for image quality assessment using natural scene statistics.

Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Traditionally, image QA algorithms interpret image quality as fidelity or similarity with a "reference" or "perfecft" image in some perceptual space. Such "full-referenc" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by arbitrary signal fidelity criteria. In this paper, we approach the problem of image QA by proposing a novel information fidelity criterion that is based on natural scene statistics. QA systems are invariably involved with judging the visual quality of images and videos that are meant for "human consumption." Researchers have developed sophisticated models to capture the statistics of natural signals, that is, pictures and videos of the visual environment. Using these statistical models in an information-theoretic setting, we derive a novel QA algorithm that provides clear advantages over the traditional approaches. In particular, it is parameterless and outperforms current methods in our testing. We validate the performance of our algorithm with an extensive subjective study involving 779 images. We also show that, although our approach distinctly departs from traditional HVS-based methods, it is functionally similar to them under certain conditions, yet it outperforms them due to improved modeling. The code and the data from the subjective study are available at.     

Image information and visual quality.

Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Image QA algorithms generally interpret image quality as fidelity or similarity with a "reference" or "perfect" image in some perceptual space. Such "full-reference" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by signal fidelity measures. In this paper, we approach the image QA problem as an information fidelity problem. Specifically, we propose to quantify the loss of image information to the distortion process and explore the relationship between image information and visual quality. QA systems are invariably involved with judging the visual quality of "natural" images and videos that are meant for "human consumption." Researchers have developed sophisticated models to capture the statistics of such natural signals. Using these models, we previously presented an information fidelity criterion for image QA that related image quality with the amount of information shared between a reference and a distorted image. In this paper, we propose an image information measure that quantifies the information that is present in the reference image and how much of this reference information can be extracted from the distorted image. Combining these two quantities, we propose a visual information fidelity measure for image QA. We validate the performance of our algorithm with an extensive subjective study involving 779 images and show that our method outperforms recent state-of-the-art image QA algorithms by a sizeable margin in our simulations. The code and the data from the subjective study are available at the LIVE website.     

高斯混合法在交通流视频中的应用

交通流视频处理是智能交通发展的重要方向,而高斯混合背景建模是背景建模领域常用的构建算法,在图像处理领域中有着广泛的应用.为了能够较为全面的分析该算法,本文对该算法进行了公式演算以及针对多个交通流量视频进行检测比较,其中提出了基于K-means、EM模型参数求解.公式演算部分能够清晰的得出该算法的数学意义和演变过程,为实际应用提供理论指导;交通流量视频处理过程可以得到该算法针对不同情况视频的效果,可分析高斯混合背景建模针对时间的迁移、光线的变化、运动物体阴影的处理能力.本文从理论及实际应用两个方面对高斯混合背景建模算法进行浅析.     

Application of fuzzy image restoration in criminal investigation

The advancement of science and technology has a positive effect on the development of law disciplines. The development of algorithms and artificial intelligence also has a certain impact on judicial practice. Image restoration is a significant technique in image processing. It aims to objectively restore the content or quality of the original image from the degraded image. Image degradation is always generated in image transmission, such as distortion, blur. In modern video surveillance system, image restoration is significant for criminal investigation. However, image restoration based on conventional filter algorithms cannot achieve satisfactory performance. Thus, we first introduce the image restoration algorithms based on different degradation model. Then, we propose some applications of fuzzy image restoration in criminal investigation. We conduct experiments on both degraded images and videos and experimental results have shown the effectiveness of fuzzy image restoration applying to the criminal investigation.