【作者】 潘刚；

【导师】 孙济洲；

【作者基本信息】 天津大学 ， 计算机应用技术， 2002， 博士

【摘要】 图像适配问题是计算机图形学和图像处理领域的重要研究方向。传统适配方法通过直接缩放图像尺寸以适应目标屏幕，会导致适配过程中图像主要内容的扭曲变形，或丢弃了图像中大量的重要信息，无法将图像的真实内容高效、准确、全面的传递给用户。基于语义的图像显示适配技术通过对图像内容进行理解识别，可以有效避免传统方法的弊端，成为近来热门的研究课题之一。本文在系统论述图像适配问题的理论基础、技术特点、国内外研究现状、研究难点、应用的基础上，提出了对图像进行分类的方法，并就三种图像类型：纹理图像，一般自然图像和具有对称特性的图像，分别提出了三种图像适配方法。首先对于自然图像，设计一种可逆的显示适配方法。该方法针对自然图像多次适配操作过程中出现的误差累计问题，提出由正向和逆向两部分能量函数组成的可逆能量函数，以解决自然图像的可逆显示适配问题。同时，针对现有方法在图像放大过程中出现的走样问题，构建了适合图像放大过程的“虚拟点”数据结构，用以预测在图像放大过程中填充像素点对整个图像适配过程的影响，从而大大提高了图像放大质量。其次对于纹理图像，基于纹理图像纹理单元尺度性强且结构性脆弱的特点，本文提出利用纹理合成的方法，实现纹理图像基于语义的适配过程，以灵活得到不同目标尺度的纹理图像。鉴于纹理合成时间和速度之间的矛盾，提出了基于小波多分辨率金字塔的纹理合成方法，在保证纹理合成质量的同时，大大提高了纹理合成的速度。同时提出了改进的纹理邻域匹配策略，并采用IMED距离进行邻域相似度的度量。最终在多种图像上进行实验，该方法可以取得比较满意的效果。再次，具有对称性质的图像是日常生活中常见的一类图像。通过分析现有四类典型方法，本文分别提出了针对平移对称图像和旋转对称图像的基于语义的对称图像显示适配方法。通过对对称单元的分析识别，采用图像概要的方式，将图像中的对称单元进行增减，从而实现整个对称区域的显示适配。同时，对于适配后的对称与非对称临界区域，利用Deghosting方法进行边界融合，使得最终结果视觉自然无明显形变。最后，总结了本文的工作，从研究内容，研究方法和研究结果三方面进行阐述，并对未来工作方向进行预测。更多还原

【Abstract】 Image retargeting is an important research area of computer graphics and imageprocessing. Previous methods scale images into target size, but fail to keep the imagein a semantic way. Images maybe shrink badly and main parts of images will bedistorted during the resizing process. At the other hands, some approaches only retainthe important parts and discards the surrounding to fit the display resolution, thisusually discard large number of background information. The retargeting methodswithout semantic feature usually lead to user’s misunderstanding. Image retargetingbased on semantic recognizing considers image content in the process of imageresizing, which is now one of the research focuses.This thesis first surveys the existing works on image retargeting. The surveyincludes theoretical basis, technical characteristics, related work, difficulties,development and applications. Based on three kinds of specific images (texture,nature image and image with symmetry), the thesis proposes different retargetingmethods.For natural images, seam carving is an effective operator supportingcontent-aware resizing for both image reduction and expansion. However, repeatedseam removing and inserting processes lead to excessively distortion image whenimposed on seam insertion and removal operations or the other way around. Byconsidering the relationship between seams removing and inserting processes, wepresent an ameliorated energy function to minimize aliasing.“Forward Energy” isonly an effective improvement to image reduction. Moreover, the thesis propose anovel”Visual Points” structure which distinguishes the “Forward Energy” of seaminsertion from that of seam removal, and improves seam insertion operations greatly.The thesis introduces texture synthesis method to texture image retargeting.Time consumption and quality are two main concerns to texture synthesis algorithm.A wavelet based texture optimization approach is proposed in this paper. Twomulti-resolution texture pyramids are used: an input pyramid built by the wavelettransform of the exemplars and an output pyramid reconstruction from the inversewavelet transform. In the step of nearest neighborhood searching, wavelet coefficientis integrated to estimate neighborhoods’ distance, instead of RGB and other channels.Because the wavelet transform is reversible and nondestructive, this strategy does notdebase quality. Images with symmetry exist everywhere. Symmetry summarization can resize atranslational symmetric image while maintaining the semantics quite well. The thesispresents a novel retargeting method that allows arbitrarily resizing the ratio ofrotational symmetric images as well as preserving their symmetric structure andseamlessness. First, we detect the rotational symmetric area, and convert the area intolinearly translation symmetric form. Then, we construct a texture feature map andextract potential symmetric cells through symmetry group analysis. The resizingprocess is finally achieved by increasing or removing proper-measured symmetriccells. Both the experimental results and user study demonstrate superior performanceof our algorithm compared with other existing methods.At last, some examples are illustrated applying the proposed algorithm ondifferent kinds of images, which concludes the thesis with discussions of the digitalresults and limitations of the presented method.更多还原