基于区域对比度和SSIM的图像质量评价方法

在诸多图像质量评价方法中,结构相似度(SSIM)算法简单高效,准确性较高,但SSIM模型不能很好地评价存在局部失真和交叉失真类型的图像。针对SSIM算法对图像不同区域平等对待的不足并考虑了时域人眼视觉特性,提出一种改进的基于区域对比度和结构相似度(RCSSIM)的图像质量评价方法。该算法将图像区域灰度信息对比度与SSIM算法融合,加权归一为参考图像与失真图像的对比度结构相似度值,以其评价图像质量。在LIVE图像数据库上的实验结果表明,与SSIM算法相比,RCSSIM评价结果的皮尔逊线性相关系数提高约0.015,均方根误差减小约0.55,更接近于人眼主观测试结果,具有更好的评价性能。     

基于图像内容视觉感知的图像质量客观评价方法

图像质量客观评价在图像和视频传输、编解码以及服务质量中起着非常重要的作用.然而现有的方法往往没有考虑图像内容特征及其视觉感知,使得其质量客观评价与主观感知结果存在一定的差距.基于此,本文结合图像内容的复杂性特征和人眼的掩蔽特性、对比敏感度特性以及亮度感知的非线性特性,提出了一种基于人眼对图像内容感知的图像质量客观评价方法.该方法首先结合亮度感知的非线性模型将图像进行转换,得到人眼感知强度图;再分别以人眼对比敏感度值和图像局部平均对比度值作为权重因子对强度求和,以求和的数据信息作为人眼感知图像的内容,并构建图像感知模型;最后以此模型分别模拟人眼感知参考图像和失真图像,并计算二者的强度差,以强度差为评价分数的基础构建图像质量客观评价模型.采用LIVE,TID2008和CSIQ三个数据库中的共47幅参考图像和1549幅测试图像进行仿真实验,且与SSIM,VSNR,FSIM和PSNRHVS等典型的图像质量客观评价模型进行对比分析,同时探讨影响图像质量评价的因素.结果表明:所提方法的评价分数与主观评价分数的Pearson线性相关性系数和Spearman秩相关系数值比SSIM的评价结果均有一定程度的提高,提高幅度分别平均为9.5402%和3.2852%,比PSNRHVS和VSNR提高幅度更大.综合以上表明:所提方法是一种有效可行的图像质量客观评价方法;同时,在图像质量客观评价中,考虑人眼对图像内容的感知和复杂度的分析有助于提高图像质量主客观评价的一致性,评价精度可得到进一步的提高.     

基于人眼视觉系统的假彩色融合图像质量的评价方法

随着图像融合技术的发展,各种融合算法层出不穷,而很多情况下最终的融合图像是由人眼观察的,因此基于人眼视觉系统的图像融合质量评价显得尤为重要.为了能够模拟人眼对于融合图像的感知,得到融合后图像质量的客观评价,本文提出了一种基于色差理论的假彩色融合图像质量的评价方法.首先将源图像和融合图像转化到CIE L*a*b*均匀色空间,在频域对图像进行对比度敏感函数滤波,通过计算滤波后融合图像的色差判断图像的细节信息,在一定程度上色差越大信息越丰富;通过计算融合图像与源图像的色差判断融合图像与源图像的相关性,相关性越高,融合算法越好.通过融合图像的色差大小以及与源图像的相关性两个参量,得出融合算法的优劣.实验表明,与其他评价方法相比,本文提出的评价方法与人眼观察的结果较为一致.     

一种基于梯度幅度值的图像质量客观评价方法

提出一种新的图像质量的客观评价方法,该方法用图像梯度幅值的累加和作为衡量图像质量的指标,无需原始图像作参考,适合于两幅以上同源图像对比来相对地评价图像质量。实验结果表明,该方法所用的指标能比较准确地反映图像的质量,符合人眼的视觉特征,且计算简单,易于快速实现。     

基于边缘的SSIM图像质量客观评价方法

在图像处理领域中,准确判断所处理图像的质量是一项关键技术.本文在基于结构相似度的图像质量评价方法的基础上,考虑了边缘信息对人眼感知结构的重要性,提出了基于边缘结构相似度的图像质量评价方法.实验中,对实验图像进行客观指标的提取与计算,最后统计分析实验数据.结果表明,该方法对图像质量的评价结果优于基于结构相似度的图像质量评价方法,能够正确反映图像质量且更加符合人眼的主观感受.     

局部方差在图像质量评价中的应用

将灰度图像的局部方差分布(QLS)作为表征图像结构信息的一个重要特征,对局部方差分布矩阵进行奇异值分解,计算得到相应的奇异值特征向量;通过计算降质图像与原参考图像局部方差矩阵奇异值特征向量的夹角大小度量两图像的结构相似度,实现了对降质图像的质量评价。实验结果表明:局部方差分布更能突出图像的结构特征,评价结果优于传统的均方误差(MSE)、峰值信噪比(PSNR)、结构相似度(SSIM)以及直接评价图像像素分布的奇异值分析(SVD)等方法,与人眼视觉感知效果的一致性较好。     

结合视觉注意力机制和图像锐度的无参图像质量评价方法

针对模糊图像的质量评价,提出一种新的无参图像质量评价方法,该方法结合了自底向上的视觉注意力机制和自顶向下的图像锐度评价标准。根据人眼视觉注意力机制模型,分别计算颜色、亮度和方向显著度图像,通过竞争机制得到人眼优先关注的区域; 利用无参图像锐度评价方法分别对优先关注的区域及背景区域进行评价,综合2个区域的评价结果得到最终的图像质量评价指标。利用该方法分别对相向运动过程中所产生的模糊图像和图像质量评价Live数据库中的高斯模糊图像进行了评价,结果表明:针对两类图像的评价结果与主观评价结果的相关系数均较高,其中,针对相向运动模糊图像的主客观评价结果的相关系数达到0.98。该方法能够胜任对模糊图像的客观质量评价。     

局部方差在图像质量评价中的应用

将灰度图像的局部方差分布（QLS）作为表征图像结构信息的一个重要特征,对局部方差分布矩阵进行奇异值分解,计算得到相应的奇异值特征向量;通过计算降质图像与原参考图像局部方差矩阵奇异值特征向量的夹角大小度量两图像的结构相似度,实现了对降质图像的质量评价。实验结果表明：局部方差分布更能突出图像的结构特征,评价结果优于传统的均方误差（MSE）、峰值信噪比（PSNR）、结构相似度（SSIM）以及直接评价图像像素分布的奇异值分析（SVD）等方法,与人眼视觉感知效果的一致性较好。     

一种无参考型模糊图像质量评价方法

图像的边缘信息是人眼观察和识别物体的重要特征,根据模糊图像相对于清晰图像其边缘特征发生较大变化的特点,提出了一种基于边缘锐度的无参考模糊图像质量评价方法。首先,通过文中所示方法寻找图像中的所有阶跃边缘;其次,根据一些原则选择合适的部分边缘;最终,计算这些合适边缘的锐利程度作为图像的模糊度评价依据。实验结果表明,该方法相比于全参考模型SSIM能够更好地评价高斯模糊、离焦模糊等模糊类型图像,与主观评价结构相关性强,更符合人眼视觉系统特性,并且易于实现。     

一种无参考型模糊图像质量评价方法

图像的边缘信息是人眼观察和识别物体的重要特征,根据模糊图像相对于清晰图像其边缘特征发生较大变化的特点,提出了一种基于边缘锐度的无参考模糊图像质量评价方法。首先,通过文中所示方法寻找图像中的所有阶跃边缘;其次,根据一些原则选择合适的部分边缘;最终,计算这些合适边缘的锐利程度作为图像的模糊度评价依据。实验结果表明,该方法相比于全参考模型SSIM能够更好地评价高斯模糊、离焦模糊等模糊类型图像,与主观评价结构相关性强,更符合人眼视觉系统特性,并且易于实现。     

Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary

Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the focus regions, especially when the source images captured by cameras produce anisotropic blur and unregistration. This paper proposes a new multi-focus image fusion method based on the multi-scale decomposition of complementary information. Firstly, this method uses two groups of large-scale and small-scale decomposition schemes that are structurally complementary, to perform two-scale double-layer singular value decomposition of the image separately and obtain low-frequency and high-frequency components. Then, the low-frequency components are fused by a rule that integrates image local energy with edge energy. The high-frequency components are fused by the parameter-adaptive pulse-coupled neural network model (PA-PCNN), and according to the feature information contained in each decomposition layer of the high-frequency components, different detailed features are selected as the external stimulus input of the PA-PCNN. Finally, according to the two-scale decomposition of the source image that is structure complementary, and the fusion of high and low frequency components, two initial decision maps with complementary information are obtained. By refining the initial decision graph, the final fusion decision map is obtained to complete the image fusion. In addition, the proposed method is compared with 10 state-of-the-art approaches to verify its effectiveness. The experimental results show that the proposed method can more accurately distinguish the focused and non-focused areas in the case of image pre-registration and unregistration, and the subjective and objective evaluation indicators are slightly better than those of the existing methods.     

奇异值非线性修正的低对比度红外图像实时增强

由于场景中目标与背景的温差相对较小,红外图像会存在对比度低、视觉效果差的问题,针对这一问题,提出一种基于奇异值非线性修正的红外图像对比度实时增强方法。该方法首先对红外图像进行奇异值分解得到其原始奇异值,然后采用一个对数型非线性变换对图像奇异值进行优化,最后根据修正的奇异值重构出对比度增强的红外图像。利用对数型非线性变换修正图像奇异值不仅能够有效拉伸奇异值的动态范围,同时可优化奇异值的变化梯度,使图像的能量信息得到更充分地表达,改善红外图像不良的视觉效果。实验结果表明,该方法较几种对比方法在视觉效果和客观评价方面均具有更优的增强性能;同时体现出良好的实时性,为实现红外图像的实时增强提供了新途径。     

多尺度奇异值分解的偏振图像融合去雾算法与实验

针对现有偏振去雾算法鲁棒性不强和图像增强效果有限的问题,提出一种基于多尺度奇异值分解的图像融合去雾算法.首先,利用偏振测量信息的冗余特性,采用最小二乘法,提高了传统偏振图像去雾算法中偏振信息的准确度;然后,从传统偏振图像去雾算法的局限性出发,定性分析了偏振图像融合去雾的可行性,并提出了一种基于多尺度奇异值分解的偏振图像...     

A novel image fusion algorithm based on nonsubsampled shearlet transform

To overcome the shortcoming of traditional image fusion method based on multi-scale transform, a novel adaptive image fusion algorithm based on nonsubsampled shearlet transform (NSST) is proposed. Firstly, the NSST is utilized to decompose the source images on various scales and in different directions, and the low frequency sub-band and bandpass sub-band coefficients are obtained. Secondly, for the low frequency sub-band coefficients, the singular value decomposition method in the gradient domain is used to estimate the local structure information of image, and an adaptive ‘weighted averaging’ fusion rule based on the sigmoid function and the extracted features is presented. To improve the quality of fused image, a novel sum-modified-Laplacian (NSML), which can extract more useful information from source images, is employed as the measurement to select bandpass sub-band coefficients. Finally, the fused image is obtained by performing the inverse NSST on the combined coefficients. The proposed fusion method is verified on several sets of multi-source images, and the experimental results show that the proposed approach can significantly outperform the conventional image fusion methods in terms of both objective evaluation criteria and visual quality.     

基于特征结构相似度的遥感影像融合质量评价指标

在分析现有影像融合评价指标的基础上,根据人眼视觉特性,提出一种基于特征结构相似度的遥感影像融合评价指标.首先从原始影像和融合影像中提取空间特征和光谱特征,然后综合计算空间特征和光谱特征的结构相似度作为评价指标,达到评价影像质量的目的.通过对多种传感器获取的遥感影像进行融合实验,结果表明该客观评价方法与主观评价结果一致,...     

基于平移不变剪切波变换域图像融合算法

针对传统基于多尺度变换的图像融合方法存在的缺点,提出了一种基于平移不变剪切波变换域的自适应图像融合新方法.首先,使用平移不变剪切波变换对源图像进行分解,得到低频子带及方向带通子带系数.然后,对于低频子带系数采用梯度域奇异值分解方法估计图像的局部结构信息,提出了基于提取的特征与S函数的可变加权融合策略;对于各方向带通子带系数,提出了一种基于改进的拉普拉斯能量和匹配的"加权平均"和选择相结合的系数选择策略.最后,对得到的融合系数进行逆变换得到融合图像.通过实验可以发现相比于传统的图像融合方法,本文方法得到了更高的客观指标,融合图像视觉效果更好.     

Two-scale image fusion of visible and infrared images using saliency detection

Military, navigation and concealed weapon detection need different imaging modalities such as visible and infrared to monitor a targeted scene. These modalities provide complementary information. For better situation awareness, complementary information of these images has to be integrated into a single image. Image fusion is the process of integrating complementary source information into a composite image. In this paper, we propose a new image fusion method based on saliency detection and two-scale image decomposition. This method is beneficial because the visual saliency extraction process introduced in this paper can highlight the saliency information of source images very well. A new weight map construction process based on visual saliency is proposed. This process is able to integrate the visually significant information of source images into the fused image. In contrast to most of the multi-scale image fusion techniques, proposed technique uses only two-scale image decomposition. So it is fast and efficient. Our method is tested on several image pairs and is evaluated qualitatively by visual inspection and quantitatively using objective fusion metrics. Outcomes of the proposed method are compared with the state-of-art multi-scale fusion techniques. Results reveal that the proposed method performance is comparable or superior to the existing methods.     

Image quality assessment using the singular value decomposition theorem

In objective image quality metrics, one of the most important factors is the correlation of their results with the perceived quality measurements. In this paper, a new method is presented based on comparing between the structural properties of the two compared images. Based on the mathematical concept of the singular value decomposition (SVD) theorem, each matrix can be factorized to the products of three matrices, one of them related to the luminance value while the two others show the structural content information of the image. A new method to quantify the quality of images is proposed based on the projected coefficients and the left singular vector matrix of the disturbed image based on the right singular vector matrix of the original image. To evaluate this performance, many tests have been done using a widespread subjective study involving 779 images of the Live Image Quality Assessment Database, Release 2005. The objective results show a high rate of correlation with subjective quality measurements.