图像内容感知缩放的检测方法研究

针对可用于图像篡改的内容感知缩放技术,本文提出了一种基于概率Map图统计特征的内容感知缩放检测算法.该算法利用概率Map图来反映图像是否经过内容感知缩放操作,并利用新提出的积分投影与局部统计特征来检测篡改图像.而后利用分类器进行分类训练,从而有效识别基于内容感知缩放操作的图像篡改.实验结果显示,所提算法能够区分出原始图像与篡改图像,并具有较高的正确检测率.     

基于缝裁剪和变形的图像缩放方法

提出一种既能保持图像重要内容又能较好地保持重要物体形状的图像缩放算法。该方法结合传统的缝裁剪技术和变形技术来对图像进行缩放。首先利用当前公认效果良好的基于图模型的流形排序显著性检测算法得到图像的显著度图,结合图像梯度能量等信息来构造结构更为清晰的图像重要度图;其次利用之前构造的图像重要度图并按缩放尺度的大小来确定适当的缩放方法;最后根据度量比较结果来选择经典缝裁剪方法或基于能量优化的变形方法进行图像缩放。对比实验结果表明,该方法在图像缩放时能保持重要内容和显著物体形状结构。     

显著物体形状结构保持的图像缩放方法

针对传统的缝裁剪图像缩放方法中可能出现对图像中显著物体形状结构的破坏问题,提出一种既考虑到显著物体内容保持又考虑到显著物体形状结构保持的新的图像缩放方法。该方法首先利用经典的图像显著度图模型,结合图像梯度直方图等信息构建形状结构更加清晰的图像重要度图;然后利用已构建的重要度图,对图像进行分块,按显著块的大小来确定缩放方法;最后结合经典缝裁剪方法和基于共形能量的变形方法对图像进行缩放。实验结果显示,该方法能够在图像缩放时更好地保持显著物体的内容和形状结构。     

主体大小能控的内容感知图像缩放

针对传统基于网格变形的图像缩放技术中可能出现的主体对象大小会随图像缩放而缩放的问题,提出一种既考虑网格形状,又考虑网格大小的形变量度量模型.在图像重要度约束下,通过简单的参数调整让用户选择性地控制主体对象的大小.重新定义图像重要度模型为梯度和显著度的加权平均,这样既考虑到人眼对图像的视觉关注,又考虑到对图像中结构信息的...     

重要区域保持的图像缩放质量评价方法

目的 图像缩放质量评价是评估图像非等比例缩放优劣的依据,现有主流评价方法不能客观有效地给出定量评价结果,为此提出一种基于重要区域保持的图像非等比例缩放评价方法。方法 首先,结合视觉显著度、图像边缘和超像素分割构造重要区域识别算法;然后,设计描述图像缩放前后重要区域大小和内容保持的面积变化函数和成分变化函数;最后混合两种函数提出缩放质量评价函数。结果 在RetargetMe提供的标准测试集上对本文方法进行实验验证,计算缩放质量值以评价不同缩放方法的优劣,并利用肯德尔系数度量本文方法的评价结果与主观评价的一致性,本文方法的平均肯德尔系数比当前主流客观方法高0.5%~11%。结论 与现有主流客观评价方法相比,本文方法所得到的肯德尔系数值更大,证明该方法能够更准确有效、快速、定量地评价各类图像非等比例缩放结果。     

一种基于Seam Carving的图像缩放方法

由于图像在不同环境下(特别是在文档中)出现时,常需要进行缩放与比例调整,人们对缩放质量提出了较高要求.许多典型的图像缩放方法都是对图像的像素直接进行转换,这使图像缩放的程度受到了限制.提出了一种改进的图像缩放算法,其基本思想是首先将进行原图像内容重要度计算,然后以动态规划为工具,充分利用图像内容的特点将图像进行缩放,这为图像缩放提供了一种思路.通过与传统的缩放算法做比较,验证了本算法能够获得更好的缩放效果,并通过对实验数据的分析和解释得到了若干有益的结论,为进一步的研究工作奠定了基础.     

基于弹簧变形模型的图像缩放方法

目的 随着显示设备分辨率和纵横比的多样化,内容感知图像缩放技术逐渐成为图像处理领域新的研究热点之一,为了既能实现图像有效缩放,又能较好保持图像主体区域的几何结构完整,提出一种基于弹簧近似的内容感知图像缩放方法。方法 首先,为控制图像缩放,对输入图像进行显著性检测和特征直线检测;其次,在图像上构建一个平面三角网格,视三角网格的每一条边为一根弹簧,则整个三角网格构成一个弹簧系统,可利用该弹簧系统的变形实现图像缩放,其中,根据每根弹簧所在图像区域的显著度设置弹簧的弹性系数,该系数可在缩放时有效保持图像的主体区域,避免主体内容缩放不均匀或变形;然后,以图像直线特征保持为约束,构造用于约束弹簧系统变形的目标函数;最后,利用纹理贴图技术把弹簧系统每个三角形对应的图像贴回弹簧系统,得到缩放后的目标图像。结果 为证明本文方法的有效性,对大量图像进行多比例缩放测试,并与现有方法进行比较。在同比例缩放下,本文方法可以更好地保持图像中的主体区域,以及特征直线,使放缩后的图像整体具有更好的视觉效果。本文方法的计算时间小于0.19 s,时间成本与现有方法大致相同。结论 基于弹簧系统的内容感知图像缩放方法,通过弹簧系统变形带动网格变形进而实现图像缩放,与现有的内容感知图像缩放方法相比,本文方法所处理的图像视觉效果更好,实验结果表明,本文方法能够更有效、快速地处理各种图像的内容感知缩放。     

能量均衡原则下的快速图像尺寸调整算法

为了实现高效的内容感知图像缩放,提出一种能量均衡原则下的快速算法.该算法采用多个低分辨率图像的水平垂直梯度能量的加权和计算像素能量;然后根据列能量自动选取若干较不重要的列,在能量均衡原则下对其宽度进行调整,得到内容感知图像缩放结果.实验结果表明,文中算法速度快,且能够有效地保护图像中的重点内容和主要结构.     

结合美学原则的内容感知图像缩放算法

针对目前内容感知的图像缩放算法存在美学效果考虑不足的问题,提出一种结合美学原则的缩放算法。算法首先采用图像协同分割的思想并结合视觉显著性检测获取图像的重要度图,以此作为图像重要内容的依据,在后续处理中防止其变形;其次针对移动终端常见的两类图像分别选择相适应的美学原则并建立其量化公式;借鉴经典的Seam Carving算法思想,利用美学构图原则和重要度图来指导裁剪线的复制与删除,达到图像缩放的效果。实验结果表明,与同类算法相比,该算法的缩放结果在保留原图重要信息的条件下,更具美感。     

基于DCT域视觉显著性检测的图像缩放算法*

为适应不同终端显示多样化的要求,需对接收到的图像进行缩放调整。针对现有的基于内容感知(content-aware)的图像缩放方法中视觉内容的连贯性易被破环而出现失真的问题,提出了一个基于离散余弦变换(discrete cosine transform, DCT)域的视觉显著性检测的图像缩放算法。该算法利用DCT域的视觉显著性检测模型获取视觉显著图,然后结合视觉显著图和能量分布图进行线裁剪(Seam Carving),实现了图像的缩放。实验结果表明,该算法与现有的基于内容感知的图像缩放方法相比,不仅保护了视觉显著内容,还保证了图像内容的连贯性,算法质量指数也获得明显的提高。     

Real-time content-aware image resizing

Content-aware image resizing is a kind of new and effective approach for image resizing, which preserves image content well and does not cause obvious distortion when changing the aspect ratio of images. Recently, a seam based approach for content-aware image resizing was proposed by Avidan and Shamir. Their results are impressive, but because the method uses dynamic programming many times, it is slow. In this paper, we present a more efficient algorithm for seam based content-aware image resizing, which searches seams through establishing the matching relation between adjacent rows or columns. We give a linear algorithm to find the optimal matches within a weighted bipartite graph composed of the pixels in adjacent rows or columns. Therefore, our method is fast (e.g. our method needs only about 100 ms to reduce a 768 × 1024 image’s width to 1/3 while Avidan and Shamir’s method needs 12 s). This supports immediate image resizing whereas Avidan and Shamir’s method requires a more costly pre-processing step to enable subsequent real-time processing. A fast method such as the one proposed will be also needed for future real-time video resizing applications.     

基于切缝对齐和变形校正的缝切割图像尺寸调整算法

针对图像尺寸调整的缝切割方法中存在的结构断裂和变形扭曲等问题,提出了一种基于切缝对齐和变形校正的切缝优化算法以获得更好的图像尺寸缩放效果.算法首先利用基于图割的切缝优化方法获得当前需要移除的一条切缝,然后采取动态规划算法,由粗到细地优化得到切缝上下像素之间的高精度匹配对应关系,最后通过逆向映射来对图像进行变形以校正发生扭曲甚至断裂的结构信息.实验结果表明,该方法能够很好地弥补缝切割所产生的扭曲变形,达到结构连续的视觉效果.基于切缝对齐和变形校正的切缝优化算法可用于调整图像尺寸使之与相应的显示设备相符,具有巨大的应用潜力.     

Nonhomogeneous scaling optimization for realtime image resizing

We present a novel approach for interactive content-aware image resizing. The resizing is performed on warping a triangular mesh over the image, which captures the image saliency information as well as the underlying image features. The warped triangular mesh and the horizontal and vertical scales of all triangles are simultaneously obtained by a quadratic optimization which can be achieved by solving a sparse linear system. Our approach can preserve the shapes of curved features in the resized images. The resizing operation can be performed in an interactive rate which makes the proposed approach practically useful for realtime image resizing. To guarantee a foldover free resizing result, we modify the optimization to a standard quadratic programming. A number of experimental results have shown that our approach has obtained pleasing results and outperforms the previous approaches.     

Optimal multiple-seams search for image resizing with smoothness and shape prior

Content-aware image resizing is of increasing relevance to allow high-quality image and video to be displayed on devices with different resolution. We present a novel method to find multiple seams simultaneously with global optimality for image resizing, incorporating both region smoothness and seam shape prior using a 3-D graph-theoretic approach. The globally optimal seams can be simultaneously achieved by solving a maximum flow problem based on an arc-weighted graph representation. Representing the resizing problem in an arc-weighted graph, we can incorporate a wide spectrum of constraints into the formulation, thus improving resizing results. By removing or inserting those multiple seams, the goal of content-aware image resizing is achieved. Due to simultaneous detection of multiple seams, our algorithm exhibits several good features: the ability to handle both crossing and non-crossing-seam cases, the ability to incorporate various feasible geometry constraints, and the ability to incorporate the seams importance, region smoothness and shape prior information. The proposed method was implemented and experimented on a variety of image data and compared with the state of the art in image resizing.     

Optimized image resizing using flow-guided seam carving and an interactive genetic algorithm

In this paper, we introduce a novel method for content-aware image resizing based on flow-guided seam carving. It extends the existing seam carving framework by replacing the conventional energy field with a “structure-aware” energy field that takes into account the feature orientations in the image. Guided by this new energy field, our approach excels in preserving (i.e., avoiding the distortion of) important structures in the image, such as shape boundaries. We also present a simple user interface to further optimize the resizing result based on the genetic selection process among multiple resizing operators such as scaling, cropping, and flow-guided seam carving. We show that such simple user interaction, coupled with the genetic algorithm, dramatically increases the chances of producing the user-desired outcome.     

An effective edge-preserving smoothing method for image manipulation

This paper presents a novel and effective edge-preserving image smoothing method for edge-aware image manipulation. The method formulates the smoothing as a problem of minimizing a convex object function with a constraint and an efficient solution to the optimization problem is presented. Specifically, the method provides an unified framework to regularize the edge and texture pixels in the optimization so that geometric edges representing image structures can be well retained and fine edges of texture regions are removed or suppressed. Both qualitative and quantitative experimental results on natural images and computer-generated structured images have shown the efficacy of the proposed method. In addition, the proposed method can improve the performance of many image processing and manipulation tasks including edge extraction and simplification, non-photorealistic rendering, detail and contrast exaggeration, HDR tone mapping, block-based discrete cosine transform (BDCT) artifact removal and content-aware image resizing, as demonstrated through the experiments.