【作者】 王金华；

【导师】 须德；

【作者基本信息】 北京交通大学 ， 计算机应用技术， 2010， 博士

【摘要】 随着计算机和多媒体技术的发展,医学影像、视频监控、卫星遥感和计算机视觉等各领域对图像的质量提出越来越高的需求。高质量的图像能够提供更丰富的信息和更真实的视觉感受,是很多实际应用的基础。高动态范围(High Dynamic Range, HDR)场景如何在普通显示设备真实再现是阻碍这些应用发展的一个难题。针对这一问题,论文提出了高动态范围场景可视化技术。它主要从色调映射(Tone Mapping)和多曝光融合(Exposure Fusion)两个方面进行研究：人类视觉系统(Human Visual System, HVS)是被显示图像的最终接收对象,因此,在色调映射算法中,利用HVS的特性指导HDR图像的动态范围压缩具有重要意义。根据这一思想,论文引入一种新的“双锚”(Double Anchoring)亮度感知理论,提出一种基于亮度感知理论的HDR场景再现算法。通过视觉差异预测器(Visual Difference Predictor, VDP)评价标准验证了提出算法的有效性,它不仅能较好地保持真实场景的细节信息和整体明暗效果,而且结果图像更加符合人眼对真实场景的感观体验。物体表面反射率具有低的动态范围,而场景的照度一般具备较大的动态范围,根据这一现象,研究学者提出了一种新的研究思路：把HDR图像分解成反射层和光照层,由于每层获取不同的信息和动态范围,因而对每层采取不同的处理方式,既能压缩了HDR图像的动态范围又能保持了图像原有的细节信息。基于这一思想,论文提出利用YUV颜色空间代替传统的RGB空间进行双边滤波(Bilateral Filter,BF)来实现图像分层,这样,可以降低亮度通道范围压缩处理对色度信息产生的影响。此外,通过利用像素的亮度值自适应地决定其在对数域中的底数值,使算法能够保留场景更多的细节信息。基于空间域的多曝光融合算法,通常采用单一的图像特征作为标准。但是,一个特征来衡量图像质量好坏是不全面的。为了克服这个问题,论文提出一种基于支持向量回归的多曝光融合算法(Support Vector Regression based Exposure Fusion, SVREF)。它能够建立多个特征和图像质量评价标准的映射关系。此外,在大多数曝光融合算法中,为了获取场景的色度信息,通常是红、绿和蓝三通道分别处理,这种处理方式不仅使结果图像泛灰(Gray-out),还增加了算法的时间复杂度。针对这一问题,论文提出一种新的场景色度信息获取的方法,能够较好地保留场景的色度信息。在基于频率域多曝光融合算法上,为了降低图像序列间小的平移或旋转对融合结果图像造成的光晕、模糊等影响,论文提出了一种基于可操纵金字塔(Steerable Pyramid, SP)的多曝光融合算法(标记为SPBEF)。同时,为了支持对多个输入图像同时进行融合处理,提出一种分层(Hierarchical)的融合规则,它可以大大增加融合图像的对比度。实验结果表明,SPBEF和其他曝光融合算法相比,能够得到较好的视觉效果。更多还原

【Abstract】 With the development of computer and multimedia technology, medical imaging, video surveillance, satellite remote sensing and other various fields such as computer vision ask high requirement for image quality. High quality images can provide more abundant information and more real visual perception to human eyes. It is the basis of many practical applications. How to display high dynamic range (HDR) scenes on common devices is a difficult problem to hinder these applications. In order to solve this problem, this dissertation focuses on the visualization of HDR scenes, and carries out research from two aspects:tone mapping and exposure fusion.The final receiving object for displayed images is human visual system (HVS), therefore, using the HVS characteristics to guide the dynamic rang compression for HDR images has a great significance. According to this conclusion, we introduce a lightness perception theory called "double anchoring" into the tone mapping algorithm. The effectiveness of our proposed algorithm is verified by means of an evaluation standard called HDR visual difference predictor (HDR-VDP). It can keep more details and the resulting images also accord with the perception of HVS to the real scenes.We know that the surface of object has low dynamic range; the illumination intensity of scenes usually has large dynamic range. According to this phenomenon, researchers present a new thinking for HDR images:decomposing the HDR images into reflection and illumination layers. Because each layer has different information and dynamic range, carrying out different approach can achieve the compression of dynamic range, and maintain more detail information. Based on this theory, we propose a tone mapping method, which is carried out using YUV color space instead of the commonly used RGB color space. Bilateral filter is used to decompose the HDR images into different layers in the logarithmic domain with adaptive base value. In addition, we propose an improved center/surround method to maintain more details in the dark and highlight regions.For most exposure fusion algorithms in the spatial domain, single image feature is usually served as a standard to evaluate the quality of the image. However, the single feature is not comprehensive. In order to overcome this problem, we propose an exposure fusion method, which is based on support vector regression. It can establish the mapping relation between multiple image features and the standard of the image. On the other hand, existing exposure fusion methods usually treat the R, G and B channels separately, which are very time consuming. In this dissertation, we present a novel way to get more chrominance information of the scene.In frequency domain, most exposure fusion algorithms are put forward on the assumption that the source images are aligned prior to fusion. As a result, some artifacts, such as haloing, may be caused due to the slight misalignment in the source images. In order to reduce the influence induced by the misalignment, a novel shift-invariant and rotation-invariant steerable pyramid-based exposure fusion (called SPBEF) algorithm is proposed. In addition, fusion is performed in a hierarchical fashion. It can increase the contrast of resulting images. Experiments show that SPBEF can give comparative or even better results compared to other exposure fusion algorithms.更多还原