结合人眼视觉特性的红外图像增强新技术

红外图像具有边缘模糊和对比度较低的缺点,不适合人眼观察,所以要对其进行增强.但是现有的增强方法没有考虑人眼的视觉特性,视觉效果不好.提出基于小波的多分辨分析方法和Retinex图像增强算法相结合的红外图像增强方法,对红外图像不同的高频细节进行有针对性增强,同时用Retinex算法把人眼的视觉特性融入其中,能够使得增强的红外图像光照均匀,亮度适中,更适合人眼观察.算法既增强了图像的细节,又增强了图像的对比度,实验证明:该方法解决了红外图像低对比度和细节模糊的问题.     

自适应双边滤波与方向梯度的红外图像增强

为克服现有的红外图像增强方法存在欠增强、过增强以及对比度不高等缺陷,提出了自适应双边滤波与方向梯度的红外图像增强方法。对双边滤波进行改进,加权系数自适应于平滑区域和细节区域,以作为Retinex的中心环绕函数,将红外图像分解为基础层和细节层;用改进的平台直方图均衡化对基础层图像进行增强;提出一种方向梯度算子,用其提取细节层图像的梯度图,进而对细节层图像进行非线性的自适应边缘增强。实验结果表明,相对于部分现有的方法,本文方法能更有效地提升红外图像的亮度和对比度,增强图像的视觉效果更佳。     

引导滤波与像素重分布的低照图像增强

针对现有的图像增强方法存在欠增强、过增强以及对比度低等缺陷,提出了一种引导滤波与像素重分布的低照图像增强算法。方法充分利用引导滤波的边缘保持特性,用引导滤波对光照图像进行估计,然后对光照图像的像素进行相对均匀重分布,全面提升光照图像的亮度与对比度。最后将像素重分布增强处理后的光照图像和反射图像作反Retinex变换,得到最后的增强图像。实验结果证明,相对现有的图像增强方法,本文方法具有更优的图像增强效果,图像对比度与纹理结构更清晰。     

基于双域分解的夜间车辆红外图像研究

夜间车辆交通红外图像光照不均,导致车辆图像细节纹理较弱,识别难度较大。为此,提出基于双域分解的夜间车辆交通红外偏振图像增强方法。采用改进Retinex低照度图像光照补偿算法,分解图像为低频图像与高频图像,对低频图像去雾、优化其对比度,对高频图像去噪与增强,合成低频、高频图像实现夜间车辆交通红外偏振图像增强。实验测试结果证明,对比传统方法,所提方法增强后图像亮度与对比度得以优化,且细节信息更丰富,具有理想的视觉效果。     

多尺度细节增强与自适应γ变换的图像增强

针对现有图像增强方法未能适宜地提升图像的亮度、对比度，以及保持图像自然效果的问题，提出了多尺度细节增强与自适应γ变换的图像增强方法。该方法根据多尺度的纹理结构和边缘细节特征，用引导滤波对图像进行多尺度的Retinex分解，分解为多尺度的细节层和最后的基础层；对基础层做自适应的γ拉伸，实现图像亮度和对比度的有效增强，对细节层进行多尺度的拉普拉斯增强；将增强的基础层与增强的细节层进行多尺度的Retinex反变换，实现原图像的增强。图像增强实验结果显示，相对于当前的部分最新方法，所提方法的图像增强性能更好，图像增强后的信息熵和平均梯度分别比现有的方法提升大约1.2和2.8。     

一种混合的遥感图像增强方法

根据遥感图像细节信息丰富,信息量大,目标和背景区分度小等特点,针对目前的遥感图像增强方法无法满足在去噪的同时增强图像对比度、细节和边缘的现象,提出了一种混合的遥感图像增强方法。首先将遥感图像做直方图均衡化,从全局增强图像的对比度,然后对遥感图像做NSCT变换,将其分解为一个低频子带和多个高频子带,对高频部分进行自适应阈值去噪和模糊对比度增强处理,为了防止过增强,对低频部分不做处理。最后对逆变换回空间域的图像用拉普拉斯滤波器增强图像的边缘。实验结果表明本文方法对遥感图像增强效果显著。     

一种结合单尺度Retinex与引导滤波的红外图像增强方法

针对传统红外图像增强算法中图像对比度低、细节信息丢失与过度增强等问题,提出了一种单尺度Retinex与引导滤波相联合的红外图像增强方法。首先根据Retinex算法,利用主特征提取法获取原始图像的照射分量和反射分量,对照射分量采用平台直方图增强其对比度;然后利用局部方差加权引导滤波将反射分量分解为基本层和细节层,对两层分量的图像分别进行对比度和细节增强操作;最后将各个层次的结果按照合适的权重因子进行融合得到增强红外图像。实验结果表明,相比于其他增强算法,本文所提方法能更有效地提高红外图像的整体对比度,突出其细节特征,增强后的3组图像的信息熵和平均梯度平均值分别为9.7373和5.6922,相较于原图像分别提升了2.7499和3.8296。     

一种基于平稳小波域的红外图像增强方法

针对红外图像对比度和分辩率低、噪声大的特点,提出一种基于平稳小波域的红外图像增强新方法.对高频子带中幅值较小的噪声系数进行衰减,幅值较大的边缘细节系数进行放大;对低频子带系数的幅值采用所提出的正弦函数进行伸缩处理.实验结果表明,本文提出的方法在有效地增强红外图像对比度及边缘细节的同时,又能很好地抑制背景噪声,综合性能明显优于传统的直方图均衡化和反锐化掩膜增强方法.     

基于自适应权重Retinex和小波变换的彩色图像增强算法

针对现有图像增强技术容易出现细节丢失、局部曝光不足、过曝光或颜色失真,不能兼顾对比度和色彩保真的问题,提出了基于自适应权重Retinex与小波变换结合的彩色图像增强算法(AMSR-WT)。将图像从RGB空间转换到HSI空间,对亮度分量I进行小波变换分解为低频亮度图像和若干高频亮度图像,对低频图像使用自适应权重Retinex进行增强,对高频图像使用改进的阈值去噪算法进行去噪,通过小波逆变换重构亮度分量,经过Gamma校正进一步增强对比度并转换回RGB空间得到增强图像。实验结果表明,该算法有效提高了图像对比度和颜色保真度,较好地保留了图像的细节和纹理。     

基于小波变换与Retinex的电路板红外图像增强技术

电路板红外图像发热芯片区域的增强是红外图像故障检测系统中一项重要研究内容。针对电路板某些芯片的发热量小,芯片区域红外图像与背景差异弱的特点,为提高故障检测效率,设计了一种融合小波变换与改进的多尺度Retinex红外图像增强算法。首先,通过小波变换获得不同频率的子带图像;然后,利用多尺度Retinex算法对低频子带图像进行增强处理;对于高频子带图像,利用可变阈值去噪,引入图像清晰度参数,依据参数值对高频图像进行适度增强。最后,对处理后的高、低频图像进行小波逆变换与重构,实现电路板红外图像增强。对增强后的红外图像进行的定量以及定性评价表明：与单一的利用直方图均衡算法、小波变换法以及多尺度Retinex增强算法相比,本文算法改善了某些发热芯片区域红外图像对比度低且细节模糊问题,抑制了噪声,提升了电路板红外图像整体视觉效果。     

Mobility enhancement

This article is targeted as an introduction to the physics of strained Si and the current state of the art in uniaxial strained Si MOSFET. The first part of the article explains how strain alters the valence and conduction band of Si as well as scattering rates. This is followed by a review of state-of-the-art strained techniques being implemented in 90- and 65-nm process technologies. Finally, we conclude with a discussion of the future scalability of strained Si MOSFETs in the ballistic regime and nanoscale CMOS.     

Multiscale contrast enhancement

A novel approach for image contrast enhancement is presented. A contrast measure which accounts for spatial structure information as derived within the context of a nonlinear multiscale representation generated by anisotropic diffusion is introduced. A local contrast map is computed, which is then used to enhance the original image     

Entropic contrast enhancement

A technique for the contrast enhancement of a picture is proposed. The method is derived from the entropy concept of information theory. The originality of the algorithm rests on the use of a local contrast to define the digital entropy. The basic idea of the treatment is to enhance the contrast by transforming the global entropy. The same technique can also be used for an adaptive smoothing processing.     

Gamma变换与多尺度细节增强的红外图像增强北大核心CSCD

针对现有的红外图像增强方法存在欠增强、过增强以及微小细节不清晰等缺陷,提出了一种Gamma变换与多尺度细节增强的红外图像增强方法。该方法充分利用红外图像边缘细节的多尺度特性,用多尺度的引导滤波将红外图像分解为多尺度的细节层和基础层;对基础层进行自适应的Gamma变换,Gamma指数自适应于基础层图像亮区像素数与暗区像素数的比值,旨在适宜地调整红外图像的整体亮度,使得隐藏于暗区的图像信息得以正常显示;对多尺度的细节层分别进行四邻域的拉普拉斯增强,以提升多尺度细节信息的信号强度。实验数据证明,相对于部分最新的现有方法,本文方法具有更好的增强效果,增强图像的视觉感知效果更佳,客观的图像质量指标平均梯度和无参考图像质量指标均较高。     

Color retinal image enhancement using luminosity and quantile based contrast enhancement

Multidimensional Systems and Signal Processing - Retinal imaging is used to diagnose common eye diseases. But retinal images that suffer from image blurring, uneven illumination and low contrast...     

Neural network training enhancement

In this study we explore the use of nonlinear embedding maps to expand the dimension of the input space. The efficacy of such maps to speed training and to enhance performance is illustrated through several examples. A natural connection to nonlinear synaptic interconnects is also developed.     

Peer group image enhancement

Peer group image processing identifies a "peer group" for each pixel and then replaces the pixel intensity with the average over the peer group. Two parameters provide direct control over which image features are selectively enhanced: area (number of pixels in the feature) and window diameter (window size needed to enclose the feature). A discussion is given of how these parameters determine which features in the image are smoothed or preserved. We show that the Fisher discriminant can be used to automatically adjust the peer group averaging (PGA) parameters at each point in the image. This local parameter selection allows smoothing over uniform regions while preserving features like corners and edges. This adaptive procedure extends to multilevel and color forms of PGA. Comparisons are made with a variety of standard filtering techniques and an analysis is given of computational complexity and convergence issues.     

Statistical-model-based speech enhancement systems

Since the statistics of the speech signal as well as of the noise are not explicitly available, and the most perceptually meaningful distortion measure is not known, model-based approaches have recently been extensively studied and applied to the three basic problems of speech enhancement: signal estimation from a given sample function of noisy speech, signal coding when only noisy speech is available, and recognition of noisy speech signals in man-machine communication. Research on the model-based approach is integrated and put into perspective with other more traditional approaches for speech enhancement. A unified statistical approach for the three basic problems of speech enhancement is developed, using composite source models for the signal and noise and a fairly large set of distortion measures     

Low distortion speech enhancement

An innovative approach to speech enhancement is illustrated which minimises distortion to the underlying speech during the noise-reduction process. The key to this approach lies in the identification of whether the additive noise for a particular frequency component is constructive or destructive. Once this can be identified both multiplicative and subtractive filters can be derived using the minimum mean-square error criterion. The optimal combination of the proposed multiplicative and subtractive filter is also shown     

Successive noise enhancement reduction

The capacity of direct sequence code division multiple access (DS-CDMA) systems is limited by multiple access interference (MAI). Commercial CDMA systems regarded MAI as additive noise and employed the matched filter detectors. This technique limits the number of users that can be supported in a DS-CDMA system and causes an increase in the bit error rates for active users in the system as the number of users increases or as the spreading factor decreases. Thus, advanced signal processing solutions for interference suppression are necessary. In this paper, we propose a new multiuser detector (MUD) detector technique and compare between its performance to other popular MUD detectors for the universal mobile telecommunications system (UMTS) physical layer.