基于谱先验的图像空变盲去模糊非局部正则化方法

项目名称： 基于谱先验的图像空变盲去模糊非局部正则化方法

项目编号： No.61302178

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 黄丽丽

作者单位： 广西科技大学

项目金额： 24万元

中文摘要： 空变盲去模糊是视频监控、遥感图像、医学影像等应用领域的研究热点。该问题的高度病态性需要利用正则化方法对其进行求解。项目基于稳健函数建立鲁棒的噪声模型；挖掘观测图像中蕴含的模糊核信息，将退化图像视为作用在模糊核上的线性算子，研究该算子的谱特性，建立与观测图像相关的非参数化模糊核模型；挖掘图像中的局部结构特征多样性及非局部冗余性，通过非局部相似块编组，分析相似结构块的谱聚类形式，提出基于图像内容的自适应非局部谱先验模型；通过K-L变换将空变PSF分解为一组空不变正交分量的加权和, 加权系数由物空间PSF分布信息确定；基于所建立的噪声模型、模糊核模型及图像非局部谱先验模型，提出包括模糊核估计和图像反卷积的统一正则化变分框架；设计快速稳定的模型求解优化算法。本课题将丰富并推动图像复原建模理论和算法的发展，所提出的理论和方法可进一步推广到图像及视频的超分辨重建等应用领域，具有重要理论意义和实用价值。

中文关键词： 图像盲去模糊；模糊核估计；图像复原；稀疏性先验；非局部正则化

英文摘要： Space-variant blind image deblurring is a hot research topic in many applications, including video surveillance, remote sensing image, and medical imaging, etc. Solving this severely ill-posed task often requires regularization to yield high-quality results. First, this project establishes a robust model of noise based on the robust function. Then，we found that the blurry image itself encodes rich information about the blur kernel. Such information can be found through analyzing the spectrum property of the image as a linear operator which on the blur kernel. This analysis leads to a non-parametric priori model of the blur kernel which depends on the given blurry image. And then, there are nonlocal redundancies and diversity of locally structural features in the nature images. A nonlocal spectral priori model is proposed to fit different image contents adaptively by clustering the nonlocal similar patches and analyzing the cluster form of the induced spectrums. Next, use of K-L transform allows for modeling the space-variant PSF as a sum of orthogonal functions that are individually constant in form over the images, but whose relative amplitudes encode the PSF spatial variability. On the basis approach mentioned above, we propose a unified regularization and variational framework of both blur kernel estimation

英文关键词： blind image deblurring；blur-kernel estimation；image restoration；sparsity prior；nonlocal regularization