图像复原算法的研究

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题名	图像复原算法的研究
作者	陈曦
学位类别	工学博士
答辩日期	2009-07-02
授予单位	中国科学院研究生院
授予地点	中国科学院自动化研究所
导师	彭思龙
关键词	降晰函数辨识广义拉普拉斯模型运动模糊散焦模糊 blur identification generalized Laplacian model motion blur defocus blur
其他题名	Research on the methods of Image Restoration
学位专业	模式识别与智能系统
中文摘要	在图像的采集、压缩和传输过程中，由于物理设备和技术条件的限制，观测到的图像通常受到降质过程的影响，导致图像质量受损，不仅不利于观测，而且妨碍对其进一步处理。一般将这类降质表示为一个空间不变的低通滤波过程和一个加性随机噪声。图像复原的主要目的就是依据观测到的降质图像，恢复出原始的清晰图像。当图像降质过程的点扩散函数（即降晰函数）未知时，这类盲目图像复原问题是一个非线性问题，处理的方法主要是引入合适的未知清晰图像和降晰函数的先验知识。本文针对图像复原问题在以下几个方面展开了研究：首先，针对运动模糊所表现出来的稀疏性特点，本文采用广义拉普拉斯分布作为运动模糊的统计模型，在保持模型灵活性的同时，降低了计算复杂度。再结合自然图像的稀疏先验模型，应用最大后验估计同时进行运动模糊辨识和图像复原。通过理论分析和仿真实验，证明了该算法的有效性。其次，对于图像同时受到多种模糊影响的情况，本文提出了一种新的图像退化模型，与传统的级联方式不同，这里假定模糊核函数是散焦与运动模糊的加权和形式。为了恢复这类模糊造成的图像降质，本文在散焦模糊给定的情况下，使用广义拉普拉斯分布作为运动模糊的统计模型，并在期望最大化(EM)算法框架下估计模糊核函数；最后利用估计的模糊核函数进行图像复原。实验结果表明，该算法能够有效的估计出混合模糊，有利于提升图像复原的效果。最后，根据小波变换子带系数统计特性上的差异，本文采用上下文模型对小波系数进行分类；在最小平方误差准则下结合相应的图像先验模型，使用样条变换从训练图像中得到小波收缩去噪函数；最后对不同类别的小波系数应用不同的收缩函数进行去噪。该算法较好的保留了图像的细节，抑制了平滑区域的噪声，改善了去噪后图像的视觉效果。
英文摘要	Due to the constraints of equipments and technical conditions, images obtained from sampling, compressing, and transferring may be degraded, which cause images visually annoying and prevent them from further processing. The degradation is often modeled as a space-invariant low-pass filter with an added random noise. The goal of image restoration is to restore the original true images from the degraded ones. Blind image restoration, where the blur kernel of the degradation is unknown, is a seriously ill-conditioned problem. To identify the blur and restore the true image, the prior information about the blur kernel and the unknown true image must be introduced. In this thesis, the following image restoration problems are studied: First, the generalized Laplacian distribution is proposed to model the motion blurs for the sparseness characteristic of the blurs, which makes a proper tradeoff between high-efficiency and low-complexity. Exploited the sparseness priors of natural image and motion blur, maximum a posterior (MAP) estimation based on Bayesian theory is used to simultaneously identify blurs and restore images. The experimental results demonstrate the effectiveness of the algorithm. Second, in order to restore the images that are degraded simultaneously by various blurs, an image restoration method for the degradation model where the blur kernel is the mixture of defocus and motion blurs is proposed. Given the defocus blur, the statistical characteristic of the motion blur is exploited with generalized Laplacian model, and then the mixed blur is identified using the Expectation/Maximization (EM) algorithm. Finally the degraded image is restored with the help of the estimated blur. The proposed algorithm could identify the blur effectively, and improve visual quality of the degraded images. Third, wavelet shrinkage denoising has been investigated for a long time due to its simplicity and good results. SLT denoising generates mapping functions (MFs), also known as shrinkage functions, which are learned directly from example images using least-squares fitting. In this paper, we design MFs with the prior information properly incorporated in SLT denoising. Since coefficients in the same wavelet subband have different statistic characteristics, we first classify wavelet coefficients into different classes. Then MFs for different regions are deduced with corresponding prior model. The proposed method obtains higher PSNR (Peak Signal to Noise Ratio), a...
语种	中文
其他标识符	200618014628059
内容类型	学位论文
源URL	[http://ir.ia.ac.cn/handle/173211/6224]
专题	毕业生_博士学位论文
推荐引用方式 GB/T 7714	陈曦. 图像复原算法的研究[D]. 中国科学院自动化研究所. 中国科学院研究生院. 2009.