特征保持的图像变换研究
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摘要
随着高速发展的电子设备使得图像处理的应用范围更加广泛,图像处理研究的问题更加广泛.因为三维数字处理与图像处理的密切联系,图像处理的考虑范围不应该局限在二维信息,也可以考虑三维信息.图像变换是对图像处理算法的总结,它可以分为四个部分：空域变换等维度算法,空域变换变维度算法,值域变换等维度算法和值域变换变维度算法.其中空域变换主要指图像在几何上的变换,而值域变换主要指图像在像素值上的变换.等维度变换是在相同的维度空间中,而变维度变换是在不同的维度空间中,例如二维到三维,灰度空间到彩色空间.而在这些研究领域中,特征保持是近几年来的新研究热点,随着显著图检测的兴起,如何根据图像变换的分类来类比特征需求,如何根据不同算法的需求来制定不同的特征,如何定义合理的算子来进行检测,以及如何在图像处理算法中保持特征是特征保持应用问题的难点.本文针对图像变换的三个热点问题讨论了如何更好的定义特征算子,以及如何使用这些特征算子来更好的增强图像处理效果.
首先,第二章讨论了彩色图像灰度化问题,属于变维度的值域变换.黑白打印,使用灰度通道算法或者黑白摄影,都需要对彩色图像做去色处理.因为数据存储的空间从三维变成一维,信息存储量变小,算法就需要把彩色图像中可能丢失的信息更多的保留下来,而且重要的信息需要更多的保留.传统的算法是利用颜色空间将颜色转换到带亮度信息的通道,直接将其作为灰度图像.之后很多算法从全局或者局部两个角度出发,希望能够尽量保持梯度信息多或者显著图大的区域信息.本算法分析了全局和局部映射算法有着相互矛盾的算法目标,提出了全局和局部两种映射模型,能够更好为用户提供最优的结果.全局映射模型充分考虑了颜色的三个分量对灰度图的影响,得到了能够自适应调节系数的三向映射模型.而在局部映射模型中,充分考虑人眼感知信息的重要程度,不同于以往算法,该局部映射不仅仅依靠像素邻域梯度或者显著图的特征,从视觉中的滤波模型出发,提出了通道显著性的概念,从色度亮度饱和度中找出了最能被人眼感知的通道,能够将这个对比度最大的通道上的信息最大化的保留在灰度化结果中.
其次,第三章讨论了灰度化的一个反问题,灰度图像的彩色化问题,也属于变维度的值域变换.灰度化的反问题就是对灰度图像进行上色,将其变换为彩色图像.该算法应用在老照片上色,动漫上色,去除红眼以及医学图像上色中,可以给图像赋予更多的信息,得到更加逼近更高真实感的图像.之前的算法从颜色迁移和亮度匹配优化的两个方向着重讨论了自然图像的彩色化.但是非真实感图像彩色化的方面主要还是局限在针对纹理的图像分割然后进行单色填充,这样就不能够有丰富的色彩变化以及在边缘处光滑的过渡效果.本算法首先提出了对非真实感图像色调分析,提取了线条稀疏反映出的图像明暗关系之后,就可以使用最优化算法依靠邻域相似性进行渐变的传播,避免了用分割算法在边界处不连续的颜色突变.但色调的明暗关系并不能体现原图像线条的邻域相似性,模式能量是根据图像的纹理特性来计算非真实感图像的邻域相似性,将其与色调能量合并计算,能够得到更加准确的邻域距离,在边缘处得到更好的上色效果.
最后,第四章讨论了同维度的空域变换中的新问题,特征保持的图像适应：即如何在缩小图像的几何尺寸的同时保持图像特征,使其内容尽可能的不被破坏.目前,随着各种电子产品的问世,显示媒介更加的变化多样,出现了各种分辨率各种尺寸的屏幕.这样我们就需要根据图像的内容进行适应性的缩放,生成保持原有特征的图像.之前的算法在考虑人眼感知信息的重要程度时,仅依靠像素邻域梯度或者显著图的特征,没有从纹理角度去分析图像信息的冗余成分.近几年的工作有讨论建筑物的重复模式来达到增减建筑单元的效果,依然不能用来分析更随机性的纹理.本章中考虑到信息冗余度和感知显著性的对偶关系,提出了分析冗余信息来保持显著性特征的图像适应算法.从分析邻域相似性的算法中借鉴了纹理层提取和模式能量两个特征分析工具,得到了自然图像的平滑和纹理区域,之后将梯度和纹理两个冗余度合并得到了重要度映射,就可以对图像进行自适应的保护.本章还将该算法推广到非真实感图像的图像适应问题中.
Accompanied with the development of electric equipments, the problems solved by image processing and the applications developed become deeper and wider. Researchers are interested not only in2dimensional signals, but also3dimensional ones, because of the tight connection between image processing and mesh processing. This thesis endeavors to put the current topics into an ensemble:Image Transformation. Image transformation can be divided into four parts:equal-dimension spatial transformation, various-dimension spatial transformation, equal-dimension value transformation and various-dimension val-ue transformation. Spatial transformation means geometrical change during the process-ing while value transformation means the change of pixel values. Meanwhile, the equal-dimension transformation produces changes only in one dimension, and various-dimension transformation maps signals into a different dimension, such as2D and3D spatial space, or1D grey and3D color space. Furthermore, feature preservation is another hot research topic partly due to the growth of saliency map. Several questions like finding the connec-tion of feature in different research fields, the definition of feature according to processing requirement, finding an appropriate operation to detect the feature, and the preservation of feature during the transformation are the most difficult parts of feature-preserved prob-lems. This thesis introduces three algorithms in two fields of image transformation, which discuss the definition of image feature and illustrate how they are adopted to ameliorate images.
The second chapter is mainly related to the various-dimension value transformation: color-to-gray conversion of color images. This topic has a huge demand for the services in black-and-white print, image algorithm pre-processing and black-and-white photography. Taking the luminance channel as the grey result is the simplest way. And then some global and local methods are invented focusing on the protection of important gradient or salient region in color images. We analyze the incompatibility of global and local target and then propose two methods both in global and local perspectives. For the global part, based on some perceived intensity experiments, we present a global nonlinear tri-mapping which could assure mapping consistent from color to grayscale. This mapping fully considers the hue, luminance, and saturation information, and further preserves feature discriminability of color gradient by an optimized tri-mapping. For the local part, we capture the perceived appearance of color images in our color-to-gray conversion. Based on the Filter Theory, a novel measurement of channel-level distinction is formulated, Channel Salience, to depict the filter level of three color stimuli. This salience metric guides a contrast adjustment process to enhance the perceived grayscale in the final output with a two-steps conversion which elaborates the grayscale on selected points first and then propagates to pixels all over the images.
The third chapter discusses a reversed problem, colorization of grey images. Image colorization is a process of adding color to a monochrome image or movie, which is an active and still challenging research topic. One of the applications is an improvement in the appearance of black-and-white cartoon images. Conventional methods for natural images begin with color transfer and luminance continuity optimization framework. In the carton cases, segmentation seems to be necessary. We propose a novel approach for colorizing black-and-white cartoon images and preserving their tone properties. First, we use an edge preserving decomposition method to compute a tone map. In order to prevent boundary leakage, we compute a pattern energy term by using the pattern feature vector. Later, the tone map and the pattern map are both applied to an optimization algorithm. Only a few of color strokes are required to be specified on the input image by the user and segmentation of the image is not required.
Finally, the fourth chapter belongs to equal-dimension spatial transformation. For the sake of displaying high resolution images on a limited screen, image retargeting is getting more attention among academic and industrial communities in recent years. The former methods mainly used gradient or salience map to describe important information. Recent researches put repetitive patterns into consideration but random textures are not included. A new image retargeting method is built upon the image redundancy analysis which reduces the sole dependence upon salience map. We introduce an image decomposition frame-work to separate redundant information from an image, to avoid the disturbance from high gradient redundant regions. Then we define a texture gradient by analyzing the texture re-dundancy which indicates the detail richness and the importance of a region. We also show that the proposed algorithm is suitable for texture-rich natural images, especially cartoon images.
首先,第二章讨论了彩色图像灰度化问题,属于变维度的值域变换.黑白打印,使用灰度通道算法或者黑白摄影,都需要对彩色图像做去色处理.因为数据存储的空间从三维变成一维,信息存储量变小,算法就需要把彩色图像中可能丢失的信息更多的保留下来,而且重要的信息需要更多的保留.传统的算法是利用颜色空间将颜色转换到带亮度信息的通道,直接将其作为灰度图像.之后很多算法从全局或者局部两个角度出发,希望能够尽量保持梯度信息多或者显著图大的区域信息.本算法分析了全局和局部映射算法有着相互矛盾的算法目标,提出了全局和局部两种映射模型,能够更好为用户提供最优的结果.全局映射模型充分考虑了颜色的三个分量对灰度图的影响,得到了能够自适应调节系数的三向映射模型.而在局部映射模型中,充分考虑人眼感知信息的重要程度,不同于以往算法,该局部映射不仅仅依靠像素邻域梯度或者显著图的特征,从视觉中的滤波模型出发,提出了通道显著性的概念,从色度亮度饱和度中找出了最能被人眼感知的通道,能够将这个对比度最大的通道上的信息最大化的保留在灰度化结果中.
其次,第三章讨论了灰度化的一个反问题,灰度图像的彩色化问题,也属于变维度的值域变换.灰度化的反问题就是对灰度图像进行上色,将其变换为彩色图像.该算法应用在老照片上色,动漫上色,去除红眼以及医学图像上色中,可以给图像赋予更多的信息,得到更加逼近更高真实感的图像.之前的算法从颜色迁移和亮度匹配优化的两个方向着重讨论了自然图像的彩色化.但是非真实感图像彩色化的方面主要还是局限在针对纹理的图像分割然后进行单色填充,这样就不能够有丰富的色彩变化以及在边缘处光滑的过渡效果.本算法首先提出了对非真实感图像色调分析,提取了线条稀疏反映出的图像明暗关系之后,就可以使用最优化算法依靠邻域相似性进行渐变的传播,避免了用分割算法在边界处不连续的颜色突变.但色调的明暗关系并不能体现原图像线条的邻域相似性,模式能量是根据图像的纹理特性来计算非真实感图像的邻域相似性,将其与色调能量合并计算,能够得到更加准确的邻域距离,在边缘处得到更好的上色效果.
最后,第四章讨论了同维度的空域变换中的新问题,特征保持的图像适应：即如何在缩小图像的几何尺寸的同时保持图像特征,使其内容尽可能的不被破坏.目前,随着各种电子产品的问世,显示媒介更加的变化多样,出现了各种分辨率各种尺寸的屏幕.这样我们就需要根据图像的内容进行适应性的缩放,生成保持原有特征的图像.之前的算法在考虑人眼感知信息的重要程度时,仅依靠像素邻域梯度或者显著图的特征,没有从纹理角度去分析图像信息的冗余成分.近几年的工作有讨论建筑物的重复模式来达到增减建筑单元的效果,依然不能用来分析更随机性的纹理.本章中考虑到信息冗余度和感知显著性的对偶关系,提出了分析冗余信息来保持显著性特征的图像适应算法.从分析邻域相似性的算法中借鉴了纹理层提取和模式能量两个特征分析工具,得到了自然图像的平滑和纹理区域,之后将梯度和纹理两个冗余度合并得到了重要度映射,就可以对图像进行自适应的保护.本章还将该算法推广到非真实感图像的图像适应问题中.
Accompanied with the development of electric equipments, the problems solved by image processing and the applications developed become deeper and wider. Researchers are interested not only in2dimensional signals, but also3dimensional ones, because of the tight connection between image processing and mesh processing. This thesis endeavors to put the current topics into an ensemble:Image Transformation. Image transformation can be divided into four parts:equal-dimension spatial transformation, various-dimension spatial transformation, equal-dimension value transformation and various-dimension val-ue transformation. Spatial transformation means geometrical change during the process-ing while value transformation means the change of pixel values. Meanwhile, the equal-dimension transformation produces changes only in one dimension, and various-dimension transformation maps signals into a different dimension, such as2D and3D spatial space, or1D grey and3D color space. Furthermore, feature preservation is another hot research topic partly due to the growth of saliency map. Several questions like finding the connec-tion of feature in different research fields, the definition of feature according to processing requirement, finding an appropriate operation to detect the feature, and the preservation of feature during the transformation are the most difficult parts of feature-preserved prob-lems. This thesis introduces three algorithms in two fields of image transformation, which discuss the definition of image feature and illustrate how they are adopted to ameliorate images.
The second chapter is mainly related to the various-dimension value transformation: color-to-gray conversion of color images. This topic has a huge demand for the services in black-and-white print, image algorithm pre-processing and black-and-white photography. Taking the luminance channel as the grey result is the simplest way. And then some global and local methods are invented focusing on the protection of important gradient or salient region in color images. We analyze the incompatibility of global and local target and then propose two methods both in global and local perspectives. For the global part, based on some perceived intensity experiments, we present a global nonlinear tri-mapping which could assure mapping consistent from color to grayscale. This mapping fully considers the hue, luminance, and saturation information, and further preserves feature discriminability of color gradient by an optimized tri-mapping. For the local part, we capture the perceived appearance of color images in our color-to-gray conversion. Based on the Filter Theory, a novel measurement of channel-level distinction is formulated, Channel Salience, to depict the filter level of three color stimuli. This salience metric guides a contrast adjustment process to enhance the perceived grayscale in the final output with a two-steps conversion which elaborates the grayscale on selected points first and then propagates to pixels all over the images.
The third chapter discusses a reversed problem, colorization of grey images. Image colorization is a process of adding color to a monochrome image or movie, which is an active and still challenging research topic. One of the applications is an improvement in the appearance of black-and-white cartoon images. Conventional methods for natural images begin with color transfer and luminance continuity optimization framework. In the carton cases, segmentation seems to be necessary. We propose a novel approach for colorizing black-and-white cartoon images and preserving their tone properties. First, we use an edge preserving decomposition method to compute a tone map. In order to prevent boundary leakage, we compute a pattern energy term by using the pattern feature vector. Later, the tone map and the pattern map are both applied to an optimization algorithm. Only a few of color strokes are required to be specified on the input image by the user and segmentation of the image is not required.
Finally, the fourth chapter belongs to equal-dimension spatial transformation. For the sake of displaying high resolution images on a limited screen, image retargeting is getting more attention among academic and industrial communities in recent years. The former methods mainly used gradient or salience map to describe important information. Recent researches put repetitive patterns into consideration but random textures are not included. A new image retargeting method is built upon the image redundancy analysis which reduces the sole dependence upon salience map. We introduce an image decomposition frame-work to separate redundant information from an image, to avoid the disturbance from high gradient redundant regions. Then we define a texture gradient by analyzing the texture re-dundancy which indicates the detail richness and the importance of a region. We also show that the proposed algorithm is suitable for texture-rich natural images, especially cartoon images.
引文
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