【作者】 Bosco Wabwire（伯斯科）；

【导师】 黄东军；

【作者基本信息】 中南大学 ， 计算机应用技术， 2008， 硕士

【摘要】 超分辨率是一种从低分辨率观测结果中获得高分辨率图像或图像序列的技术。低分辨率等同低频信息,而高分辨率则包含了高、中、低各频带的信息。目前,超分辨率主要有基于重建的技术和基于学习的技术两种方式。现在研究的热点是人脸图像超分辨率合成,即从低分辨率脸部图像中获得高分辨率图像。人脸图像超分辨率合成由Baker和Kanade提出,人脸图像所隐含的高频部分必须被完全构造出来。人脸图像超分辨率合成极具挑战性,因为人们对于脸部非常熟悉。高分辨率和超高分辨率在多媒体领域应用非常广泛,传统的方法是输入几个低分辨率的图像,然后合成出来一个高分辨率的图像,问题是我们很难获得足够数量的低分辨率图像,所以这种技术应用非常有限。基于学习的超分辨率是依靠先验知识从低分辨率图像中推断丢失细节的算法。最近,在马尔科夫网络领域出现了基于学习的算法,但是大多数的算法获得图像信息的水平仍然很低,最新的研究表明如何提高获取图像信息的水平非常重要,值得深入的研究。我们即从这一点出发,在一些获取图像的技术实例和马尔科夫网络的基础上,研究人脸图像超分辨率合成技术。在本文中,我们研究的是人脸图像超分辨率技术,或者说基于样本图像和马尔科夫网络的人脸图像超分辨率合成技术。我们构造了一个高分辨率图像的统计模型,并说明如何通过该模型从一个低分辨率输入图像估计高分辨率图像。我们的目标是使超分辨率脸部图像算法更加简单、灵活和适合于实时处理。在标准的马尔科夫网络框架下,采用了一种新颖算法,即采用特定位置抑制算子来增加马尔科夫网络中观测函数的概率值;对于马尔科夫网络中的隐含节点,通过像素颜色特征匹配和特定位置抑制算子选择最接近的k个节点,在这些节点之间采用最一致的周边相容性检查技术,增加马尔科夫网络传递函数的值。在相关的训练使用样本图像并非很精确对齐的情况下,这些技术产生了良好效果。实际上,我们构造一种自适应马尔科夫网络,从而获得一种精确、快速并且较简单,灵活的算法,以适应输入图像的实时处理。本文提出的技术通过了几个实例测试,采用图像数据库进行了相关试验,演示了该算法,表明其有效性。我们发现块位置的约束机制,能够增加观察函数的概率值。特别是,即使使用一个小的训练数据集,它也有良好的表现,能够寻找到最接近的高频图像块。更多还原

【Abstract】 The term super-resolution (SR) is used to describe the process of obtaining a high-resolution (HR) image or a sequence of HR images from a set of low-resolution (LR) observations. Low-resolution is equivalent to low-frequency and high-resolution consists of high, middle and low frequency bands. There are in general two classes of super-resolution techniques: reconstruction-based (from input images alone) and learning-based (from other images). Of particular interest is face hallucination (which implies the high-frequency part of face image, must be purely fabricated), or learning high-resolution face images from low-resolution ones. Hallucinating faces is particularly challenging because people are so familiar with faces.Many multimedia applications rely on high-resolution images. Conventional methods employed several input low-resolution images to reconstruct the output high-resolution image. Since it is difficult to obtain sufficient numbers of input images, the effect of such methods is limited. Learning-based algorithms are popular SR techniques that use application dependent prior to infer the missing details in low resolution images. Learning-based algorithm under the framework of Markov Random Fields is emerging recently and attracts many researchers. However, most of such algorithms learn low-level knowledge about images. Recent researches indicate that learning high-level knowledge is also important and should be investigated further more. We follow this line and study the face image super resolution techniques based on image examples and Markov Network (MN).In this thesis, we investigate the face image super resolution technique or hallucination based on image examples and Markov Network (MN). We build a statistical model of high-resolution images and show how this model can be used to estimate a high-resolution image from a lower-resolution input image. Our goal is to make the algorithm of SR face images more simple, flexible and suitable for real-time applications. Under the framework of standard MN, we propose a novel algorithm that uses the location-restraint operation to increase the probability value of observation function in MN. For hidden nodes of MN, we use the most compatible neighbouring patches among k closest ones that are selected by combining the pixel colour feature matching and location restraint operation. This can increase the transition function of MN and works very well even under the condition where the alignment of the images in the training dataset is not accurate. By these measures, we generate an adaptive MN. Achieving accurate, fast and a simpler, flexible and suitable algorithm for realtime application of the input images is a critical step in super-resolution processing. Motivated by this basic requirement, the techniques presented in this thesis are tested in practical experiments. We carry out related experiments with the image database and demonstrate the effectiveness and performance of the proposed algorithms. Due to the simplicity of computing MN, our algorithm is practical and suitable for real-time applications. We find that location restraint mechanism can potentially increase the probability value of observation function in MN. Especially, with a small training dataset, it extremely improves the performance of searching the closest patches.更多还原