摘要
图像是最重要的视觉信息存储载体之一,也是最基本的与大众连接最紧密的信息表达形式,影响着人类对世界的认知。随着图像获取设备的普及以及移动互联网技术的发展,数字图像的数量呈几何级增长,影响到大众生活的点点滴滴。一副有灵魂的图像胜过千言万语,在社交网络的当代,人们越来越乐意于以分享图像的形式来展示自己工作、生活、情感的方方面面,好的图像渲染与展示方法成了分享表达意图的关键；另一方面,由于图像信息的爆炸,在繁杂信息中找出关键并进行突出有助于对图像的理解,其涉及的视觉重要信息检测技术也可服务于图像的渲染与展示。因此,图像的渲染展示方法与重要信息检测技术具有格外重要的研究意义。
图像渲染与展示指通过特殊的表现形式,对单张图像,或通过多张图像间的关联性给出具有良好视觉效果的图像处理方法,当前方法一般缺乏出色的动态效果并具有一定局限性。图像视觉重要信息检测找出图像中重要信息,应用广泛,是图像渲染与展示中的关键问题之一。
本文工作主要研究图像渲染与展示的若干方法,并研究与之相关的视觉重要性检测问题,重点研究了图像漫游展示方式、基于摄影元素的图像重要性检测和基于非线性滤波的选择性图像风格化。取得以下研究成果：
1.提出了一种基于镜头缩放形式的图像场景漫游展示方式,改变以往图像渲染方法缺乏动态性的问题,形成一个类似摄像镜头在不同图像场景中漫游的效果,给用户以栩栩如生的动态展示效果。根据视觉重要性分析定义了将一张图像嵌入另一张图像合适程度的衡量方法,并通过动态规划方法生成优化播放序列,提出了合适的图像融合方法可以将图像在嵌入过程中进行快速无缝融合,使最终展示效果具有较好地视觉连续性。该方法具有较好的通用性。
2.提出了基于摄影元素的图像重要性检测方法,并将其应用于生成图像拼贴等应用,方法基于“颜色”、“景深”、“构图”等摄影元素,能够一定程度上反映摄影者对图像的理解。定义了颜色间相对重要性,提高了颜色重要性检测精确度;训练了简单有效的图像景深分类器,提出的景深分析方法解决了传统方法对浅景深图像检测效果较差的问题；提出的图像构图与结构分析方法可进一步优化检测效果。方法可以服务于图像渲染、展示以及图像处理领域的多种应用。
3.提出了基于非线性滤波的选择性图像风格化与抽象化方法,提出的方法可以方便生成选择性抽象化结果,简化不必要的信息,突出重点,实现真实感与非真实感的和谐融合。将基于偏微分方程的模型与双向滤波进行结合,提出了改进的非线性滤波模型,得到抽象化结果,解决了传统抽象化方法只简化区域内部信息,对区域形状仍然进行保留的问题；模型可控制每个像素的抽象化程度,根据重要性图可生成层次抽象化效果；模型采用了各项同性滤波与各项异性滤波相结合的策略,使得方法在效果和计算速度方面较之以往方法均有提高。
Image is one of the most important carriers of visual information. As an information expression form which most closely connected with the public, image can influence the human knowledge of the world. As the spread of digital camera and the development of Mobile Internet technology, the number of digital images is increasing exponentially. An image with a soul is worth a thousand words. In the era of computer mediated social networking, people are willing to share images to show their work, life and emotion, thus, good image rendering and display method has become the key to express their intent. In addition, as the explosion of image information, identifying the importance from complex images helps the understanding of image. The visual importance detection technology can also serve image rendering and display. Therefore, the image rendering/display and importance detection methods have important research significance.
Image rendering and display operate on single or multiple images to give appealing visual effect. Current methods are lack of dynamic effect and are limited to some applications. Important information detection of image is widely used and is one of the key issues of image redering and display.
This dissertation focuses on some key issues of image redering, display and visual importance (saliency) detection, which include:image wandering display method, image saliency detection based on photo elements and selective image abstraction. Our contributions are summarized as follows:
1. It presents an approach to wandering in the image scenes based on camera zoom style. The method greatly increases the dynamic effect and enjoyment of image display. It switches between images scenes in a continuous zoom-like process. According to visual importance analysis, the criteria to measure how well an image can be used as a patch to be embedded into another are defined, based on which a dynamic programming algorithm is proposed to generate the optimal playing sequence. We propose an effective composition method to embed the next image to the previous one to produce visual coherent output. The method has good versatility.
2. It proposes a novel method for image saliency detection based on photo elements like color, depth of field and composition. This method can convey the photographers's idea of a scene. It defines the relative importance between colors, which improves the accuracy of color saliency. Because traditional methods work poorly on low depth of field images, a simple but efficient classifier is trained to improve the detection results of such images. The detection result will be father refined by an image composition and structure analysis step. This method can serve a variety of applications in image rendering and display fields like photo collage generation.
3. A selective image stylization method based on nonlinear diffusion is presented. The proposed method can generate selective abstraction results efficiently: simplifies unnecessary information and emphasizes the importance, ahieves the harmonious mixture of reality and stylization. It combines PDE (Partial Differential Equation) method with bilateral filtering and proposes an improved nonlinear diffusion model for abstraction, which simplifies inside regions as well as region boundaries. The model can control the degree of abstraction based on importance map and generate levels of abstraction effect. The model adopts the strategy which combines the isotropic filtering with anisotropic one. This method is faster than the previous models and can produce more sophisticated abstraction results.
引文
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