一种改进的Seam Carving图像缩放算法

针对图像非比例缩放时出现的失真,基于Seam Carving算法进行了研究:重新定义了显著能量,以突出图像的重要内容;优化了基于能量图的查找方法,通过减少像素线穿越图像中重要内容的次数,以保护图像重要内容不被扭曲;结合双线性插值以限制抽取像素线的数目,以避免图像内容过度抽取。实验结果表明,改进算法能够更好的保护图像中的重要内容、提高图像缩放的质量,并提升Seam Carving算法的执行效率。     

图像自适应中基于形变控制的细缝剪裁方法

为了降低图像自适应过程中图像内容缺失和图像扭曲变形,文章中提出了一种基于形变控制的图像自适应细缝裁剪方法,将融合局部和全局显著性的显著图作为细缝裁剪参考的能量图,对需要剪裁的细缝按照这个能量图进行排序,以更好地保护图像中感兴趣区域（ROI）,保留图像的主要内容信息;同时,利用SIFT流矢量场来衡量剪裁图像的形变程度,每移除一定数量的细缝就计算剪裁图像与原始图像之间的形变,一旦形变达到某一阈值,就停止细缝裁剪,转换为均匀缩放使图像到达目标尺寸。实验结果表明,文章中提出的方法更好地平衡了均匀缩放和非均匀剪裁,更有利于保留图像主要内容和避免主要内容的变形。      

基于梯度矢量方向性分析的线裁剪算法

在传统线裁剪(seam carving)算法中梯度矢量的方向性是一个被忽略的因素,该文提出了一种新的基于梯度矢量方向性分析的线裁剪算法。首先利用随机纹理区域内梯度矢量方向散乱的特点,对局部区域的图像梯度矢量进行低通滤波,使算法能够提取更加合理的像素线路;接着又根据像素线路的走向不同,定义两个不同的像素能量函数,给予像素梯度矢量的x, y分量以不同的权值。实验结果图像显示,文中算法不仅可以更好地保护图像边缘等细节,还可以在整体上达到与原图像更加近似的视觉效果。定量分析结果也显示,相比其它算法该文算法在完整性距离和一致性距离两方面都取得了更好结果。     

支持可变分辨率的SPIHT编码算法

为了适应移动多媒体通信中终端设备显示尺寸多样性的要求,本文将图像缩放嵌入编码过程中,提出了一种基于线裁剪（seam carving, SC）的支持可变分辨率的多级树集合排序（set portioning in hierarchical trees,SPIHT）图像编码算法。该算法在构造新的基于根节点的空间方向树的基础上,利用SC生成基于块的线能量图来引导编码,获得空域可伸缩的码流;解码端仅需获取与终端设备显示尺寸相关的码流即可完成解码和图像缩放。实验结果表明,当编码和解码图像的分辨率一致时,本文算法的率失真（rate-distortion, R-D）性能逼近传统的SPIHT算法;当解码图像的分辨率可变时,本文算法在压缩码率与重建图像的主观质量上均优于传统的SPIHT算法。      

基于NSCT结合显著图与区域能量的红外与可见光图像融合

针对传统的红外与可见光图像融合出现的清晰度和对比度偏低,目标不够突出的问题,本文提出了一种基于Non-subsampled Contourlet(NSCT)变换结合显著图与区域能量的融合方法。首先,使用改进的频率调谐（Frequency-tuned, FT）方法求出红外图像显著图并归一化得到显著图权重,单尺度Retinex(Single-scale Retinex, SSR)处理可见光图像。其次,使用NSCT分解红外与可见光图像,并基于归一化显著图与区域能量设计新的融和权重来指导低频系数融合,解决了区域能量自适应加权容易引入噪声的问题;采用改进的“加权拉普拉斯能量和”指导高频系数融合。最后,通过逆NSCT变换求出融合图像。本文方法与7种经典方法在6组图像中进行对比实验,在信息熵、互信息、平均梯度和标准差指标中最优,在空间频率中第一组图像为次优,其余图像均为最优结果。融合图像信息量丰富、清晰度高、对比度高并且亮度适中易于人眼观察,验证了本文方法的有效性。     

基于最小位移可视差的连续Seam Carving算法在图像缩放中的研究

图像缩放技术要求对图像缩放的同时保证重要信息不丢失且物体边缘不发生扭曲。近年来,Seam Carving及其改进算法得到了广泛的关注和研究。由于采用了离散式最小能量线迭代搜索策略,缩放信息无法在迭代过程中传递导致扭曲现象普遍存在。该文针对上述问题提出最小位移可视差(JND)检测算法,能够有效地检测每一次迭代中出现的潜在扭曲信息。能量权重\begin{document}$ {E}_{w} $\end{document}能够将JND信息累加传递给后续的迭代过程,从而抑制缩放过程中的边缘扭曲现象。通过JND算法和能量权重,该文首次将离散的Seam Carving模型转变为连续缩放模型。最后,在公共数据集RetargetMe上与最新的图像缩放算法进行多组对比实验,验证了所提方法的有效性和先进性。     

顽健合成图像篡改检测及定位

针对自然图像与高度仿真的计算机生成图像的合成图像篡改检测问题,提出在YCbCr颜色空间基于差分直方图和中心对称局部二进制模式提取图像块颜色和纹理特征的方法,通过训练后验概率支持向量机模型对待测图像块进行识别.在不重叠分块情况下先大致判断篡改区域,然后在该区域内逐像素分块判别,最终实现篡改区域精确定位.实验结果表明,对128 dpi×128 dpi图像块的识别率达到94.75％,高于现有方法;对合成图像篡改区域能够实现精确定位,且对旋转、缩放操作表现出较好的顽健性.     

基于重要度扩散和自适应采样的图像/视频缩放技术

提出了一种基于重要度扩散和自适应采样的图像和视频自适应缩放方法,它在整体概貌和重要区域保护之间进行折衷处理。重要度扩散函数将删除像素的重要性扩散至其邻域,以避免过多删除非重要区域而造成图像整体概貌失真。自适应采样函数则通过对各行和列像素的重要性进行权值的采样,以保护重要区域。此外,通过引入帧间一致性约束,该算法也适合于视频缩放。仿真实验结果表明:与剪切、Seam Carving等方法相比,本算法取得了较好的缩放效果。     

基于按列分块和混合分块的压缩感知图像重构方法

压缩感知理论由于其可通过低采样率来恢复原始信号的特点,近年来逐步被用于光学成像领域。为了解决在对成像图像进行压缩感知重构时数据量过大、计算负担大的问题,分块压缩感知的方法被提出。在该方法的基础上做出改进,依次提出了按列分块和混合分块的压缩感知方法。其中按列分块的方式改变了分块模式,降低分块时的要求,混合分块则结合两种分块的特点,有效提升了压缩感知的效果。通过仿真实验验证,本方法有效提升了图像重构质量,尤其是混合分块方式,在图像的重构速度和重构质量上都有显著提升。     

基于相机标定的非对称裁剪检测算法

该文提出一种针对图像非对称裁剪的取证方法,算法通过相机标定的方法来估计主点位置,并根据主点的位置对图像中存在的非对称裁剪进行指证。该文根据图像取证需求改进了相机标定模型,通过选取不共面的规则图形作为标定物,实现了在不用对标定物进行测量和建模的前提下通过单幅图像对主点坐标进行估计。实验表明,该文提出的算法对非对称裁剪篡改具有较好的检测效果。     

Sparse Seam-Carving for Structure Preserving Image Retargeting

The seam carving technique is one of the most popular techniques for content-aware image retargeting. There have been many attempts to improve existing methods of seam carving to provide more aesthetic retargeting results. However, most previous methods focus on developing an energy map for preserving important image regions while retargeting. Commonly, the existing seam carving based techniques tends to produce discontinuity artifacts when retargeting straight structures. We observe that it occurs when a bunch of seams are assigned across straight structures (i.e., lines or edges). In this paper, we propose a modified seam carving method not only preserving important parts, but also maintaining the prominent structure of the image. To this end, we put a constraint that seams are sparsely assigned each other. Experimental results show that the proposed sparse seam carving yields more plausible retargeting results than previous retargeting approaches and it is very robust to images containing prominent lines or edges.     

Cluster-Based Saliency-Guided Content-Aware Image Retargeting

Straightforward image resizing operators without considering image contents (e.g., uniform scaling) cannot usually produce satisfactory results, while content-aware image retargeting aims to arbitrarily change image size while preserving visually prominent features. In this paper, a cluster-based saliency-guided seam carving algorithm for content-aware image retargeting is proposed. To cope with the main drawback of the original seam carving algorithm relying on only gradient-based image importance map, we integrate a gradient-based map and a cluster-based saliency map to generate a more reliable importance map, resulting in better single image retargeting results. Experimental results have demonstrated the efficacy of the proposed algorithm.     

Saliency-based content-aware lifestyle image mosaics

In this paper, a novel content-aware lifestyle image mosaic technique is proposed based on image saliency. The image saliency is used in the whole process of creating mosaics. Firstly, a novel energy map for variable-size tile decomposition is proposed by combining Neighborhood Inhomogeneity Factor and Graph-Based Visual Saliency. The target image is divided into small tiles with variable sizes based on the energy map. Secondly, saliency-weighted image retrieval is introduced to choose the tile images from a certain image database. Different aspect ratio between the tile and the corresponding tile image may lead to obvious distortion. Therefore, before filled into the tile, the chosen tile image is resized by seam carving to change its aspect ratio. At last, color correction is done on the mosaic result to improve the color similarity. Compared with other mosaic methods, the proposed technique creates much better mosaics in visual aspect.     

A new image quality assessment method to detect and measure strength of blocking artifacts

Block based transform coding is one of the most popular techniques for image and video compression. However it suffers from several visual quality degradation factors, most notably from blocking artifacts. The subjective picture quality degradation caused by blocking artifacts, in general, does not agree well with the popular objective quality measure such as PSNR.A new image quality assessment method that detects and measures strength of blocking artifacts for block based transform coded images is proposed. In order to characterize the blocking artifacts, we utilize two observations: if blocking artifacts occur on the block boundary, the pixel value changes abruptly across the boundary and the same pixel values usually span along the entire length of the boundary. The proposed method operates only on a single block boundary to detect blocking artifacts. When a boundary is classified as having blocking artifacts, corresponding blocking artifact strength is also computed. Average values of those blocking artifact strengths are converted into a single number representing the subjective image quality. Experiments on various JPEG compressed images with various bit rates demonstrated that the proposed blocking artifacts measuring value matches well with the subjective image quality judged by human observers.     

Saliency detection using joint spatial-color constraint and multi-scale segmentation

In this paper, a novel method is proposed to detect salient regions in images. To measure pixel-level saliency, joint spatial-color constraint is defined, i.e., spatial constraint (SC), color double-opponent (CD) constraint and similarity distribution (SD) constraint. The SC constraint is designed to produce global contrast with ability to distinguish the difference between “center and surround”. The CD constraint is introduced to extract intensive contrast of red-green and blue-yellow double opponency. The SD constraint is developed to detect the salient object and its background. A two-layer structure is adopted to merge the SC, CD and SD saliency into a saliency map. In order to obtain a consistent saliency map, the region-based saliency detection is performed by incorporating a multi-scale segmentation technique. The proposed method is evaluated on two image datasets. Experimental results show that the proposed method outperforms the state-of-the-art methods on salient region detection as well as human fixation prediction.     

Salient edge and region aware image retargeting

The purpose of image retargeting is to automatically adapt a given image to fit the size of various displays without introducing severe visual distortions. The seam carving method can effectively achieve this task and it needs to define image importance to detect the salient context of images. In this paper we present a new image importance map and a new seam criterion for image retargeting. We first decompose an image into a cartoon and a texture part. The higher order statistics (HOS) on the cartoon part provide reliable salient edges. We construct a salient object window and a distance dependent weight to modify the HOS. The weighted HOS effectively protects salient objects from distortion by seam carving. We also propose a new seam criterion which tends to spread seam uniformly in nonsallient regions and helps to preserve large scale geometric structures. We call our method salient edge and region aware image retargeting (SERAR). We evaluate our method visually, and compare the results with related methods. Our method performs well in retargeting images with cluttered backgrounds and in preserving large scale structures.     

基于深层特征学习的可压缩感知及缝雕刻的图像重定向

多媒体技术的飞速发展推动了图像处理与显示设备 的应用与发展,为了使图像在不同的设备上进行最佳显示,需要对图像的尺寸进行调整。因 此,本文提出一种基于深层特征学习的可压缩感知及接缝雕刻的图像重定向方法。首先从预 先训练的VGG-19网络中提取输入图像的深度特征图,从最深层开始 计算特征图像的可压缩率,根据计算的可压缩率运用接缝雕刻的方法在特征域(Feature fie lds Seam Carving,FSC)调整特征图的大小,然后依次向较浅的层传播,得到所有特征层的 重定向图像后,将输入图像对应于第一层特征图的去缝的位置处的像素去掉,得到原始图像 的重定向图像。若没有达到目标图像的大小,最后再进行均匀缩放(scaling,SCL)。在Retar getMe数据集上分别进行主观与客观评估,结果表明,与其他方法相比,本文的重定向方法 总体上实现了更好的性能。