Nonlinear filtering structure for image smoothing in mixed-noise environments

This paper introduces a new nonlinear filtering structure for filtering image data that have been corrupted by both impulsive and nonimpulsive additive noise. Like other nonlinear filters, the proposed filtering structure uses order-statistic operations to remove the effects of the impulsive noise. Unlike other filters, however, nonimpulsive noise is smoothed by using a maximum a posteriori estimation criterion. The prior model for the image is a novel Markov random-field model that models image edges so that they are accurately estimated while additive Gaussian noise is smoothed. The Markov random-field-based prior is chosen such that the filter has desirable analytical and computational properties. The estimate of the signal value is obtained at the unique minimum of the a posteriori log likelihood function. This function is convex so that the output of the filter can be easily computed by using either digital or analog computational methods. The effects of the various parameters of the model will be discussed, and the choice of the predetection order statistic filter will also be examined. Example outputs under various noise conditions will be given.     

Radiosity with Well Distributed Ray Sets

In this paper we present a new radiosity algorithm, based on the notion of a well distributed ray set (WDRS). A WDRS is a set of rays, connecting mutually visible points and patches, that forms an approximate representation of the radiosity operator and the radiosity distribution. We propose an algorithm that constructs an optimal WDRS for a given accuracy and mesh. The construction is based on discrete importance sampling as in previously proposed stochastic radiosity algorithms, and on quasi Monte Carlo sampling. Quasi Monte Carlo sampling leads to faster convergence rates and the fact that the sampling is deterministic makes it possible to represent the well distributed ray set very efficiently in computer memory. Like previously proposed stochastic radiosity algorithms, the new algorithm is well suited for computing the radiance distribution in very complex diffuse scenes, when it is not feasible to explicitly compute and store form factors as in classical radiosity algorithms. Experiments show that the new algorithm is often more efficient than previously proposed Monte Carlo radiosity algorithms by half an order of magnitude and more.     

基于森林火灾平台的红外图像识别技术研究

森林火灾探测一直是森林资源保护工作中的老大难问题。早期的火灾探测技术多是基于火灾的烟雾和温度特征的,但其判别标准过于单一,误报率较高。随着计算机技术的发展和红外探测器的发明,出现了基于红外图像处理的森林火灾防护技术,该技术主要对红外探测器获得的森林现场的红外图像进行一定的处理后识别图像中是否有火灾出现。本文的主要工作是进行红外图像增强算法的研究,通过对各种算法的描述和仿真实验结果分析,提出一种相对较适合森林背景红外图像的处理算法一将基于频域处理的提升小波变换与直方图修正结合起来的红外图像增强算法。     

基于像素拓展的图形分割与合成的方法研究

提出了一种新的图像分割与合成方法。对于图像的分割,提出了新的分割算法,先是手工将一些明显属于物体和明显属于背景的像素分割出来,然后创建一个小矩形区域进行像素拓展迭代,迭代到图像边界为止。对于图像的合成,采用平均法,设计了新的算法,使物体的基本颜色保持不变的同时,降低了合成图像中从源图像分割的物体与新背景的差异,并使得其饱和度、色度、强度更加自然真实。     

基于互联网图像集的室外场景建模技术综述

目前在互联网上有海量的室外场景照片,它们不仅内容覆盖面广,对同一场景有大量不同视点和光照条件下的采样,而且获取成本很低.如何利用这些照片中包含的丰富场景信息,快速、方便地构造各种逼真的虚拟场景,是互联网迅猛发展给虚拟现实、计算机图形学和计算机视觉带来的新的研究课题之一.文中分析、总结了近年来国内外互联网图像的真实场景几何建模、自然光照建模和材质反射属性建模的最新研究进展,并对其未来的发展趋势提出了一些看法.     

基于AROP程序包的类Landsat遥感影像配准与正射纠正试验和精度分析

遥感影像的精确配准和正射纠正是进行图像融合、变化检测、图像镶嵌、定量遥感建模、多时相和多传感器影像协同应用的基础和前提。以美国国家航空和航天管理局下设LEDAPS(Landsat Ecosystem Disturbance Adaptive Processing System)课题组开发的配准与正射纠正程序包AROP(Automated Registration and Orthorectification Package)为例,详细阐述了其配准的原理与程序设计流程,并对其配准的精度进行了分析和评价。试验表明:AROP程序包算法能够找出足够的控制点,且控制点分布较为均匀,配准误差小于0.5个像元。误差特征表现为:扫描误差明显大于航向误差,误差的结果与影像漂移、DEM、坡度存在一定的相关性,高程和坡度是影响配准精度的主要因素之一。该程序包目前能够用于对我国CBERS影像的正射校正以及波段不匹配处理,但是对HJ卫星CCD影像数据配准还有待于进一步研究。     

视错觉及其应用

视觉足人类获取外部信息的最重要方式。但由于人类自身的生理、心理因素以及外界光、形、色等因素的干扰,会产生视觉误差,即视错觉。本章系统的分析了视错觉产生原因,按不同的现象和成因把视错觉分成病理视错觉、动态视错觉、几何视错觉、颜色视错觉、轮廓错觉等5类。在此基础上介绍了视错觉的应用,特别介绍了其在图像融合中的应用,并用matlab实现了图像的融合。     

图形图像制作软件中的色彩模式分析

色彩模式是在数字处理过程中处理色彩的基本标准,其是以颜色模型为基础,利用数字定量对颜色进行描述的方法。针对图形图像制作软件中的色彩模式进行分析。     

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.