一种基于HWD的图像质量评价测度

提出了一种新的图像质量评价测度.首先对图像进行Hybrid Wavelets and Directional Filter Banks (HWD)分解,得到相应的尺度和方向子带信息.然后对各个子带进行对比敏感度掩模,使不同尺度和方向子带的信息具有相同的灵敏度.根据人类视觉感知特性设定视觉感知阈值,获得相应的视觉感知系数并归一化.利用参考图像和失真图像视觉感知系数差值的变化得到相应的图像质量评价测度.实验结果表明本测度与主观感知有很好的一致性,能准确地反映人眼对图像的视觉感知.     

一种基于人脸区域的图像质量客观评价算法

提出了一种基于人脸区域的图像质量客观评价模型算法,该算法将整个图像区域划分为感兴趣区域(人脸区域)和不感兴趣区域,对这两个区域内的图像损伤,在计算中分别赋予不的权重,以体现人眼的感知兴趣.实验证明通过该模型算法得到的客观评价结果与人眼的主观感觉具有更好的一致性.     

基于虚拟仪器技术的X射线成像系统

设计了一套X射线成像系统.该系统以虚拟仪器技术为设计思想,采用调用动态链接库驱动图像采集卡进行图像采集,在Labview软件平台下进行X射线图像信号的处理,并结合相应的硬件设备,得到X射线透视后的图像,实现了X射线对不透明物体的检查.该方案既利用了Labview图形化编程的方便和虚拟仪器强大的开发功能,又降低了系统的开发成本.实验结果证明了该方案的可行性.     

基于非局部梯度的图像质量评价算法

图像质量评价研究的目标在于模拟人类视觉系统对图像质量的感知过程,构建与主观评价结果尽可能一致的客观评价算法。现有的很多算法都是基于局部结构相似设计的,但人对图像的主观感知是高级的、语义的过程,而语义信息本质上是非局部的,因此图像质量评价应该考虑图像的非局部信息。该文突破了经典的基于局部信息的算法框架,提出一种基于非局部信息的框架,并在此框架内构建了一种基于非局部梯度的图像质量评价算法,该算法通过度量参考图像与失真图像的非局部梯度之间的相似性来预测图像质量。在公开测试数据库TID2008, LIVE, CSIQ上的数值实验结果表明,该算法能获得较好的评价效果。

     

一种基于Contourlet变换的图像质量评价算法

由于基于Contourlet变换的图像处理已经成为多尺度、多方向图像处理领域的重要组成部分,所以能对嵌入Contourlet变换域图像处理系统中的图像质量进行评估的算法就成为重要的评估方法。通过对Contourlet变换域图像结构特性的研究,提出了Contourlet结构相似度(CSSIM)的图像质量评价方法。在LIVE图库上的实验结果表明CSSIM比SSIM和DWTSSIM(基于小波的SSIM)更加符合人眼的视觉特性,可以更好地评判图像质量。因此该算法可应用于基于Contourlet变换的图像处理系统中。     

一种基于小波分析的部分参考型图像质量评价方法

部分参考型图像质量客观评价方法已经成为图像质量评价领域研究的热点之一。该文利用视觉感知特性,通过统计图像经小波分解后视觉感知系数在各子带中的变化情况,提出了一种基于小波分解的部分参考型图像质量评价方法。该方法与经典的RR-WISM(Reduced-Reference image quality assessment using Wavelet-domain natural Image Statistic Model)方法相比,主观感知的相关系数平均提高3%,主观感知的离出率平均降低6%,传输数据量减少50%,计算代价大大降低。实验结果表明提出的方法与主观感知有很好的一致性。     

基于递变能量线性约束的X射线图像质量评价方法

X射线图像质量评价是射线检测正确与否的关键问题,传统的人工主观评价方法效率低,实时性差,常用的对比度、清晰度等考核指标,受成像过程的散射及噪声影响,存在一定的局限性.为此论文提出基于递变能量线性约束的X射线图像质量评价方法.文章首先通过分析递变能量成像过程中图像序列的灰度变化规律,提出序列间灰度增益分布图,并依据成像器件的线性最优成像,从分布图中提取线性散布度评价图像质量;进一步针对过曝光影响散布度评价失败问题,综合线性散布度和平均对比度,构建了新的图像质量综合评价标准,实现射线图像质量评价.最后通过实验验证了论文方法的可行性,能够有效避免传统单一评价指标的局限性.     

针对对比度变化的图像质量评价方法

由于对比度变化容易引入图像亮度和色彩等失真,本文提出了一种面向对比度变化的图像质量评价方法CCIQA。所提方法先将图像进行亮度和色度分离,再分别根据亮度强度变化和明暗对比度变化提取亮度失真因子和根据色度相似性提取色度失真因子,接着依照基于亮度强度的权重图进行融合并计算得到最终图像质量评价分数。所提CCIQA方法在4个常用的数据库,TID2008,TID2013,CID2013和CCID2014进行广泛测试。实验结果表明所提CCIQA算法符合人眼视觉对对比度变化的主观感知,且算法性能优于多个最新图像质量评价方法。      

基于颜色名称的彩色图像质量评价

图像质量评价(Image Quality Assessment, IQA)是计算机视觉领域研究的基本问题之一。目前,绝大多数图像质量模型都是基于灰度图像构建的,而彩色图像质量评价至今依然是IQA领域的开放性问题。彩色图像质量评价研究的关键在于建立与人类色彩认知能力相一致的色彩信息的量化描述。本文基于颜色名称(Colornames, CN)构建彩色图像质量评价模型,将图像的每个像素值映射为CN概率向量,利用Wasserstein距离计算两幅图像的感知色差,以亮度和梯度特征作为补充,在池化阶段采用显著性加权得到客观图像质量评分。在公开测试数据集上的实验结果表明,提出的模型在TID2008、TID2013和最新的KADID-10k数据集上表现最佳,其SROCC值分别为0.900 9,0.890 1,0.863 7。总体评价效果与目前最好的传统方法(非深度学习方法)相当;而对于颜色失真,则具有明显的优势。     

高能射线成像技术在中药农药残留检测中的应用

为了提高中药中农药残留的检测能力,提出基于高能射线成像技术的中药中农药残留检测方法,构建农药残留检测的高能射线图像采集模型,对中药农药残留的高能射线图像进行三维稀疏性重构,结合中药农药残留表面特征重建方法进行高能射线成像的纹理特征分析,构造中药农药残留检测的高能射线图像进行多尺度特征分解,采用小波检测模型进行机器视觉分析,实现对中药农药残留的高能射线图像边缘轮廓检测检测和显著特征点提取,根据中药农药残留表面的特征点提取结果,通过高能射线成像技术实现对中药农药残留的优化检测。仿真结果表明,采用该方法进行中药农药残留检测的准确性较高,对农药残留的块匹配检测能力较好,提高了对中药农药残留检测能力。     

A weighted full-reference image quality assessment based on visual saliency

In full reference image quality assessment (IQA), the images without distortion are usually employed as reference, while the structures in both reference images and distorted images are ignored and all pixels are equally treated. In addition, the role of human visual system (HVS) is not taken account into subjective IQA metric. In this paper, a weighted full-reference image quality metric is proposed, where a weight imposed on each pixel indicates its importance in IQA. Furthermore, the weights can be estimated via visual saliency computation, which can approximate the subjective IQA via exploiting the HVS. In the experiments, the proposed metric is compared with several objective IQA metrics on LIVE release 2 and TID 2008 database. The results demonstrate that SROCC and PLCC of the proposed metric are 0.9647 and 0.9721, respectively,which are higher than other methods and it only takes 427.5 s, which is lower than that of most other methods.     

Visual-PSNR measure of image quality

Objective assessment of image quality is important in numerous image and video processing applications. Many objective measures of image quality have been developed for this purpose, of which peak signal-to-noise ratio PSNR is one of the simplest and commonly used. However, it sometimes does not match well with objective mean opinion scores (MOS). This paper presents a novel objective full-reference measure of image quality (VPSNR), which is a modified PSNR measure. It will be shown that VPSNR takes into account some features of the human visual system (HVS). The performance of VPSNR is validated using a data set of four image databases, and in this article it is shown that for images compressed by block-based compression algorithms (like JPEG) the proposed measure in the pixel domain matches well with MOS.     

畜产品品质的高光谱图像无损检测研究进展

应用新兴的高光谱图像技术对畜产品品质进行无损检测是当前的研究热点之一。介绍了该技术的基本原理和分析过程,总结分析了国内外畜产品品质高光谱图像无损检测的研究现状和存在问题,提出了畜产品品质高光谱图像技术检测的未来研究发展方向,以期对我国相关研究人员的研究工作提供参考。     

No-reference stereoscopic images quality assessment method based on monocular superpixel visual features and binocular visual features

No-reference quality assessment of images has received considerable attention. However, the accuracy of such assessment remains questionable because of its weak biological basis. In this paper, we propose a novel quality assessment model based on the superpixel index and biological binocular mechanisms. The technical contributions of our model are the introduction of local monocular superpixel features and three global binocular visual features. We utilize monocular superpixel segmentation to extract two types of entropies as the local visual features for accurate quality-aware feature extraction. In addition, natural scene statistics features are extracted from the binocular visual information to complement the local monocular features and quantify the naturalness of the stereoscopic images. Finally, a regression model is learned to evaluate the quality of the stereoscopic images. Experimental results from three popular databases demonstrate that the proposed model has a more reliable performance than earlier models in terms of prediction accuracy and generalizability.     

Perceptual visual quality assessment using deeply-learned gaze shifting kernel

Image quality assessment (IQA) is a useful technique in computer vision and machine intelligence. It is widely applied in image retrieval, image clustering and image recognition. IQA algorithms generally rely on human visual system (HVS), which can reflect how human perceive salient regions in the image. In this paper, we leverage both low-level features and high-level semantic features to select salient regions, which will be concatenated to form GSPs by the designed saliency-constraint algorithm to mimic human visual system. We design an enhanced IQA index based on the GSPs to calculate the simialrity between reference image and test image to achieve image quality assessment. Experiments demonstrate that our IQA method can achieve satisfactory performance.     

Hierarchical image resampling detection based on blind deconvolution

Resampling detection is a helpful tool in multimedia forensics; however, it is a challenge task in cases with compression and noisy. In this paper, by modeling the recovery of edited images using an inverse filtering process, we propose a novel resampling detection framework based on blind deconvolution. Different interpolation types in the resampling process can be distinguished by our algorithm, which is significant for practical forensics scenarios. Furthermore, in contrast to traditional resampling detection algorithms, our method can effectively avoid interference caused by JPEG block artifacts. As the experimental results show, our method is more robust than other state-of-the-art approaches in the case of strong JPEG compression and substantial Gaussian noise.     

基于视觉感知特性的红外图像质量评价方法

根据红外成像特点,设计了一种基于视觉感知特性的红外图像质量评价算法。该算法结合人眼视觉和红外图像的结构信息对图像的失真程度进行描述,通过提取图像的边缘特征、对比度特征,然后利用视觉显著模型对特征进行差异融合,从而实现对红外失真图像的质量评测。实现结果表明,本文方法可对失真红外图像进行有效评价,与传统方法相较,此评价指标与人眼主观感知更一致。     

A survey of perceptual image processing methods

Perceptual approaches have been widely used in many areas of visual information processing. This paper presents an overview of perceptual based approaches for image enhancement, segmentation and coding. The paper also provides a brief review of image quality assessment (IQA) methods, which are used to evaluate the performance of visual information processing techniques. The intent of this paper is not to review all the relevant works that have appeared in the literature, but rather to focus on few topics that have been extensively researched and developed over the past few decades. The goal is to present a perspective as broad as possible on this actively evolving domain due to relevant advances in vision research and signal processing. Therefore, for each topic, we identify the main contributions of perceptual approaches and their limitations, and outline how perceptual vision has influenced current state-of-the-art techniques in image enhancement, segmentation, coding and visual information quality assessment.     

基于图像分割的地雷目标检测

前视地表穿透雷达能够对车前安全距离外的区域成高分辨率图像,利用前视地表穿透雷达进行地雷探测是一个复杂环境下的微小目标检测问题。受非平稳背景干扰,传统检测算法探测性能有限。该文提出一种基于图像分割的背景估计及地雷目标检测方法。连续多帧图像用来估计出耦合信号,为降低计算量,将成像后估计改进为回波域估计后成像。对剔除耦合信号后的图像实施均衡以保证各处增益相同,均衡后图像利用二维Otsu 算法分割出能量较强的区域,降低后续检测过程中强目标对杂波统计的影响,最终获得比传统检测算法更好的探测性能。该文同时还提出一种快速算法用于实时系统。通过实测数据验证,该方法可以有效改善前视地表穿透雷达的探测性能。      

基于小波变换的低对比度图像增强

针对传统算法存在噪声过增强的问题,提出了基于小波分析的图像增强算法。在小波变换多尺度分析的基础上,算法对图像多尺度分解得到的小波系数进行缩减去噪,然后在不同尺度上对各分解系数进行不同程度的增强;对同一尺度的系数进行非线性处理以增加对比度;增强低频子带图像的对比度以保证整体的增强效果。实验表明,该算法能有效地增强低对比度图像,减小了噪声的增强幅度,使结果图像具有很好的视觉效果。