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图像去模糊相关问题研究
Alternative TitleResearch on the Key Methods for Image Deblurring
黎智辉
Subtype工学博士
Thesis Advisor彭思龙
2014-05-21
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword图像去模糊 频率域相对误差 扩展卷积 三维卷积 加权保真项 Image Deblurring Frequency Domain Relative Error Extended Convolution 3d Convolution Weighted Likelihood Term
Abstract图像去模糊问题是一个典型的反问题。受制于反问题的内在约束,在其庞 大的解空间中寻找真解或者符合视觉习惯的解都非常困难。再加上观测过程中 引入的噪声,更是制约解的质量。 针对图像去模糊的反问题特性,从模型上来看,已有的方法主要集中在两 个大的方向:1)寻求更恰当的图像先验知识来构造更精确的先验模型。这些 先验经历了从光滑性、分片光滑性、梯度稀疏性等诸多特性的演变,在图像的 盲目和非盲目复原方法中都广泛应用。2)根据对观测噪声的分析设计更合理 的保真项。如基于噪声高斯分布的欧几里得范数、稀疏分布的ℓ1范数等。总体 来看,目前关于先验模型的讨论较保真项的研究更多。然而保真项约束了复原 结果的忠实程度,在模型中占重要分量。已有的保真项模型并没有过多的考虑 与图像有关的一些特性,如图像边缘结构信息、成像信息等。 为此本文从保真项模型出发,探讨了图像复原中几个方面的问题: 1)在深入分析图像复原中振铃效应特性的基础上,提出针对图像复原的 频率域相对误差概念。该误差项能够更好的反映出复原结果中振铃效应,并且 与模糊核有较强的相关性。由此提出了基于频率域相对误差的去模糊模型。该 模型可以通过小波域分解进行近似,然后使用通用的方法方便的求解。 2)在遮挡效应模糊图像复原情况下,通过深入分析遮挡效应的影像,发 现其与边界效应近似,由此提出了基于扩展卷积的模糊模型来处理遮挡情况的 模糊问题。该模型将遮挡情况转变为线性模型描述,从而可以利用通用的优化 方法方便的求解。3)针对空间变化运动模糊问题,研究了旋转/缩放运动模糊的特性,通过 引入时间维,提出了基于三维卷积的旋转/缩放运动模糊模型。其对应的复原 问题变为三维反卷积,其中的核心部分为构造三维模糊图像,可以根据模糊图 像的特性构造出来。此外还尝试了运动参数的盲目估计。 4)通过分析一些去模糊方法得到的复原结果误差的特性,发现其中主要 部分为高频部分误差。由此提出通过高通滤波器约束高频误差的加权保真项模 型。给出了权重参数和高通滤波器的导出方法。该高通滤波器的使用与预条件相对应,由此加快收敛速度。优化的权值有效的约束了高频成分误差,改进了 解的准确性。
Other AbstractImage deblurring is a typical inverse problem. For the reason of ill-pose in inverse problem, it is hard to find the true solution, or the solutions that fit our perceptual, in the huge solution space of image deblurring problem. Furthermore, the observation noise enlarge the difficulty. As an inverse problem, the deblurring problem has two partitions in their optimization model. One is the prior model which reflects the knowledge about image. The progress of the prior model includes smooth, picewise smooth and sparse in gradient domain et al. These prior was used wildly in blind and nonblind deblurring. The other is the likelihood model based on the observation noise distribution. Such model include euclidian norm, ℓ1 norm et al. The properties about image, such as edge structure information and knowledge about camera, were not considered enough in the exist likelihood term. In this thesis, we start from the likelihood term to discuss the following problem in deblurring: 1)Based on analyzing ringing artifacts, we introduce the relative error in frequency domain to image deblurring. This error model reflects the ringing artifacts in deblurring result significantly, and has strong relationship to the blur kernel. Then the deblurring model was proposed by using relative error in frequency domain and can be approximated by wavelet decomposition. The approximated model can be solved by some common optimization methods. 2)When the blur image include occlude artifacts, we propose the extended convolution model to handle it. This model derives from our observation that the occlude is equivalent to the boundary condition in image. Our model is a linear system and can be solved by exist optimization methods. 3)In spacial-variant motion blur problem, the 3-D convolution model is proposed to describe rotation and zoom blur. The key idea for our method is introducing the time dimension to the model. Then, the deblurring problem forrotation/zoom blur was converted to 3-D deconvolution problem. The main problem in our deblurring method is constructing 3-D blur images, which can be constructed using blur property. And we estimate the blur parameters. 4)We analyze the main component of MSE and present a weighted likelihood form to solve the deblurring problem. The weighted likelihood term is proved to have more suitable properties, which can reduce solution error, than traditional simple likelihood term. With the use of the optimal weight parameter and the high-...
shelfnumXWLW1967
Other Identifier200918014628029
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/6587
Collection毕业生_博士学位论文
Recommended Citation
GB/T 7714
黎智辉. 图像去模糊相关问题研究[D]. 中国科学院自动化研究所. 中国科学院大学,2014.
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