夜视融合图像质量主观评价方法

为建立通用、客观的融合图像质量评价方法,在分析图像质量主观评价方法基础上,研究了融合图像质量主观评价对象、评价条件、评价指标和数据处理等关键环节,结合目标探测识别与场景理解两个典型的视觉任务,提出了目标可探测性和细节可分辨性两个主观评价指标,并研究了图像整体感知质量与这两个指标的相关性。对3种不同场景的微光与红外融合图像,采用9种融合方法获得的189幅融合图像的统计分析结果显示,细节可分辨性和图像整体感知质量相关性好,而目标可探测性虽然和图像整体感知质量相关性较差,但在基于具体视觉任务的融合图像质量评价过程中,仍可作为有效评价指标之一。     

可见光与红外融合的汽车抗晕光图像评价方法

为了解决汽车抗晕光场景中,融合图像质量的客观评价与人眼视觉效果不一致的问题,提出了一种新的可见光与红外融合图像质量评价方法,该方法通过设计自适应迭代阈值法自动确定融合图像的晕光临界灰度值,并将融合图像自动分为晕光区和非晕光区。针对晕光区,设计晕光消除度指标评价晕光消除的效果;针对非晕光区,从多方面评价色彩、细节信息的增强效果,并甄选出合适的指标构成完整的图像质量评价体系。为验证该方法的合理性,对采用4种不同算法的融合图像进行评价,实验分析表明,该方法的主客观评价结果一致,适于评判不同可见光与红外融合的抗晕光图像质量及算法的优劣。     

小波域结构相似度融合图像质量评价方法

针对融合图像质量评价问题,分析了图像质量评价与融合图像质量评价的关系,给出了融合图像质量评价方法的一般表达公式,指出构造实际并不存在的参考图像是解决融合图像质量评价问题的关键。在此基础上,基于空域结构相似度评价方法,对输入源图像和融合图像分别进行小波分解,利用输入源图像小波分解系数构造参考图像小波系数,然后根据人眼视觉敏感度带通特性对参考图像和融合图像的各小波频带进行加权,从而得到整幅图像的小波域结构相似度评价指标,利用目标可探测性、细节可分辨能力和图像整体舒适性构成主观评价指标分别和交互信息量、基于空域的结构相似度比较。实验结果表明,相比于传统的客观评价方法,提出的方法所得结果与主观评价结果的一致性更好。     

全参考图像质量指标评价分析

图像是人类从外界获取信息的重要来源，通过客观的图像质量评价，能帮助人们在浏览图像时关注高质量的图像，提升感观体验。因此，如何对大量的图像质量进行客观而有效的评价，已成为图像信息处理领域的研究热点之一。根据对参考图像（即无失真的原始图像）的依赖程度，客观图像评价方法可分为全参考图像质量评价、部分参考图像质量评价和无参考图像质量评价。针对全参考图像质量评价方法进行分析，比较了常见的4种全参考图像质量评价指标，验证了这些评价指标分数与主观视觉评估相一致。     

基于场景理解的双波段彩色融合图像质量评价

图像质量评价是双波段彩色融合处理算法及系统评价的基础,文中研究了一种可见光与红外彩色融合图像质量评价方法。提出了基于场景理解的图像感知质量PQSU 综合评价指标,选择三类典型场景彩色融合图像进行了主观视觉评价实验; 通过对已有评价指标与PQSU 综合指标的主观评价实验结果进行多元线性回归分析,建立了PQSU 的预测模型。结果表明:融合图像的场景颜色协调性与自然感高度相关;利用图像清晰度和颜色协调性可以有效地预测PQSU;针对不同场景类型,已有评价指标在PQSU 的预测模型中所占的权重有所不同,但预测模型的基本形式保持不变。文中提出的PQSU 及其预测模型为进一步发展融合图像质量客观评价模型奠定了基础。     

生成式双波段彩色融合图像质量评价

为了解决融合图像数据量有限的问题,提出了一种生成式双波段彩色融合图像质量评价方法。该方法基于对抗生成网络生成伪彩色融合图像,在训练过程中考虑了结构的不相似性,为伪彩色融合图像加入不同程度的无序变量生成新的数据集。使用孪生网络在伪彩色融合图像数据集中进行预训练,在真彩色融合图像数据集中进行微调,对彩色融合图像质量进行评价。实验结果表明,生成式双波段彩色融合图像质量评价方法的计算结果与人眼主观感受具有较高的一致性,可以有效地对彩色融合图像进行客观评价。     

基于参数估计的无参考型图像质量评价算法

针对现有客观图像质量评价算法通用性差及人类主观感知相关性弱等问题,该文提出一种基于参数估计的无参考型图像质量评价算法。该算法通过分析图像本身所蕴含的质量特征,提取出图像结构信息、颜色信息和视觉信息的评价度量指标,采用回归分析的方法估计出相应指标的参数。实验结果表明,该算法与主观评价结果一致性好、鲁棒性强,优于其它客观评价算法,能够准确反映人类对图像质量的视觉感知效果。     

夜视抗晕光融合图像自适应分区质量评价

针对夜视晕光场景中,高亮度晕光信息导致现有红外与可见光融合图像评价方法失效的问题,该文提出一种自适应分区的融合图像质量评价方法。该方法根据可见光图像的晕光程度自动确定自适应系数,并通过迭代计算可见光灰度图像的晕光临界灰度值,将融合图像自动分为多个晕光区和非晕光区;在晕光区由设计的晕光消除度指标评价融合图像的晕光消除效果;在非晕光区从融合图像自身特性、对原始图像信息保留程度以及人眼视觉效果3方面评价融合图像纹理色彩等细节信息的增强效果;通过对4种不同抗晕光算法的融合图像进行评价分析,甄选出9种客观评价指标构成夜视抗晕光融合图像质量评价体系。不同夜视晕光场景下的实验结果表明,所提方法能够全面、合理地评价红外与可见光融合的抗晕光图像质量,解决了融合图像晕光消除越彻底客观评价结果反而越差的问题,也适于评判不同抗晕光融合算法的优劣。     

图像质量评价的研究进展和若干问题的解决途径

针对当前图像质量评价的研究现状,对各种评价方法进行了比较分析和归纳总结。利用U-Texas at Austin的LIVE图片库中的样本对几种常用的客观评价方法进行了测试,利用该数据库中得到广泛认可的主观评价分数,能够衡量客观评价指标与主观评价的符合程度,从而对这些评价指标的单调性、准确性和一致性进行了辨析和阐释,指出了其适用性和局限性。并利用VQEG推荐的方法对论断给出了实例印证。进而根据发展趋势和应用需求,对图像质量评价方法在应用中所必须面对的一些问题和解决途径提出了初步的见解。     

图像融合质量评价方法研究

图像融合的清晰度直接影响到图像融合系统的实用性.在分析现有主客观图像质量评价方法的基础上,提出了以亮度、对比度和信息量为基础的图像融合质量评价方法.利用多种图像融合算法,对红外与微光图像融合后的结果进行了质量评价.实验表明,该方法是一种实用的、有效的车载图像融合质量评价方法.     

Perceptual quality assessment for multimodal medical image fusion

Recent years have witnessed that the multimodal medical image fusion (MMIF) plays critical roles in clinical diagnostics and treatment. Many MMIF algorithms have been proposed to improve the MMIF images quality. The quality of multimodal medical fused images will significantly affect the results of the clinical diagnosis. However, little work has been designed to evaluate the effectiveness of MMIF algorithms and the quality of MMIF images. To this end, this paper presents a perceptual quality assessment method for MMIF. A MMIF image database (MMIFID) is first built to employ the classical MMIF algorithms, and the subjective experiment is conducted to assess the quality of each fused image. Then, a no-reference objective method is proposed for the perceptual quality evaluation of MMIF images,which uses Pulse Coupled Neural Network (PCNN) in Non-subsampled Contourlet Transform (NSCT). A fused image is decomposed by NSCT into low frequency sub-band (LFS) and high frequency sub-band (HFS). It is used to motivate the PCNN processing, and large firing times are employed to measure LFS and HFS. Finally, two components evaluation results are combined to obtain the overall objective quality score. Experimental results based on the MMIFID indicate that our presented method outperforms the existing image fusion quality evaluation metrics, and it provides a satisfactory correlation with subjective scores, which shows effectiveness in the quality assessment of medical fused images.     

Metric for multimodal image sensor fusion

A novel objective non-reference metric for multimodal image sensor fusion is presented. Using the joint segmentation of the input images and entropy of the regions as the priority, an `ideal' fused image is obtained. The metric is based on computing how accurately the important regions from the input images are transferred into the fused image. Experiments have shown that the obtained metric values correspond better to the subjective quality of the fused images than other state-of-the-art fusion metrics     

基于多流融合生成对抗网络的遥感图像融合方法

由于强大的高质量图像生成能力,生成对抗网络在图像融合和图像超分辨率等计算机视觉的研究中得到了广泛关注。目前基于生成对抗网络的遥感图像融合方法只使用网络学习图像之间的映射,缺乏对遥感图像中特有的全锐化领域知识的应用。该文提出一种融入全色图空间结构信息的优化生成对抗网络遥感图像融合方法。通过梯度算子提取全色图空间结构信息,将提取的特征同时加入判别器和具有多流融合架构的生成器,设计相应的优化目标和融合规则,从而提高融合图像的质量。结合WorldView-3卫星获取的图像进行实验,结果表明,所提方法能够生成高质量的融合图像,在主观视觉和客观评价指标上都优于大多先进的遥感图像融合方法。     

Image quality assessment based on edge preservation

Objective image/video quality metrics which accurately represent the subjective quality of processed images are of paramount importance for the design and assessment of an image compression and transmission system. In some scenarios, it is also important to evaluate the quality of the received image with minimal reference to the transmitted one. For instance, for closed-loop optimization of a transmission system, the image quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original image – prior to compression and transmission – is not usually available at the receiver side, and it is important to rely at the receiver side on an objective quality metric that does not need reference or needs minimal reference to the original image. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art reduced reference metric.     

Multimodal medical image fusion by cloud model theory

Image fusion can provide more extensive information since it combines two or more different images. Cloud model is a recently proposed theory in artificial intelligence and has the advantage of taking the randomness and fuzziness into account. In this paper, we introduce a novel multimodal medical image fusion method by cloud model theory. The proposed method fits the histograms of input images using the high-order spline function firstly and then divides intervals in line with the valley point of the fitted curve. On this basis, cloud models are generated adaptively through the reverse cloud generator. Finally, cloud reasoning rules are designed to achieve the fused image. Experimental results demonstrate that the fused images by proposed method show more image details and lesion regions than existing methods. The objective image quality assessment metrics on the fused images also show the superiority of the proposed method.     

一种基于Curvelet变换的图像融合新算法

基于Curvelet变换提出了一种新的融合算法.首先将图像进行Curvelet 变换,然后对粗尺度系数和细尺度系数采用不同的融合规则将Curvelet 系数融合,最后进行重构得到融合结果.对得到的融合图像进行了主客观评价和对比,实验结果表明,本文方法得到的融合图像,在熵、均方误差(MSE)、峰值信噪比(PSNR)和相似度等客观指标上都优于其他方法.