【作者】 王伟；

【导师】 郑津津；

【作者基本信息】 中国科学技术大学 ， 精密仪器及机械， 2014， 博士

【摘要】 数码相机的时代已经到来,其革命无处不在,数字图像也是。然而,相机对图像质量的提高并非总能跟得上人类的苛求。图像质量的降低称为图像降质,有许多原因导致,最主要是由于模糊和噪声。随着相机镜头的小型化和廉价化,受限于拍摄水平,如果没有拍摄时没有拿稳相机或者对焦不准,都会减少图像清晰度,让人很难分辨细节。一些其它的随机因素也会给相机带来不可避免的噪声。美好的瞬间一去不回,难以复制。人们迫切希望找到一些简单有效的方法来恢复图像,提高质量。我们的工作就是围绕着各种形式的图像模糊展开。我们的主要工作和创新点如下：一、首先回顾了相关背景知识和研究现状,指出了图像恢复和图像增强之间的区别,介绍了图像式恢复相对于其它恢复方式的优点,展望了其广泛的应用领域；就什么是图像恢复技术这个问题的解释,将图像恢复和图像增强之间作出对比。接着我们对图像恢复技术中的关键技术和难点进行了阐述。二、搭出基于单幅模糊图像的盲复原框架：模糊图像的盲恢复是一项极富挑战性的工作,导致其病态性的主要原因是已知量少于未知量。针对这个问题,我们试图通过增加辅助信息作为约束条件来限制未知量。具体做法是：用两个包含显著边缘的空间先验的正则代价函数来交替迭代模糊核和清晰图像直至收敛,得到模糊图像的模糊核；之后又利用包含稀疏先验的正则函数来恢复清晰图像,可以保持更多的结构细节。为减少噪声对模糊核的干扰,我们还提出模糊核的修正准则。针对前人相对盲目的人工设置模糊核尺寸,我们首次提出了自适应模糊核尺寸选择机制。三、根据各自数学模型,对几种特殊模糊作出快速处理：在直线运动模糊的对数频谱中找到其模糊参数：模糊方向和模糊尺度,并且结合误差参数法进一步做出精确估计；在离焦模糊的倒频谱的第一个中心圆环中估计出了离焦半径；用Hough变换先提取出旋转运动模糊图像边缘中的有效圆弧段,接着用最小二乘拟合出圆心位置,然后依据Bresenham画圆算法提取出各个同心圆上的像素,分别对其进行一维复原,最后镶嵌回去得到完整的二维去模糊图像；针对局部模糊,用一系列的形态学方法先分割出模糊区域,接着用盲复原框架算法对模糊区域进行恢复,最后同原清晰区域整合得到完整去模糊图像。四、针对更加病态的含噪模糊图像,首先利用方向高通滤波器和Radon逆变换来重建模糊核。然后在去噪去卷积阶段,我们建立两个代价函数交替迭代直至收敛,最终收敛得到去噪去卷积的清晰图像。五、针对模糊图像质量的评价,首次提出了考虑主观感受的客观评价标准。我们不仅考虑了一些客观评价准则诸如饱和度、对比度、边缘信息以及基于自然统计规律的功率谱知识,而且有效地将对人眼视觉敏感的图像纹理考虑进来,组成了一个加权评价函数形式。在如何确定各项评价准则的权重因子时,我们用到了最小二乘非线性回归拟合方法。更多还原

【Abstract】 The age of digital cameras is dawning, and its revolution is everywhere, so is digital image. However, the advancement of image quality due to camera cannot always keep up with men’s expectation. We refer to reducment in image quality as image degradation; there are many reasons for this, which mainly due to blur and noise. With the miniaturization and cheapness of camera lens, and limited to photographers’level, if the camera doesn’t be hold steadily or the object get out of focus, all of which can reduce the visibility of image, and make us hard to distinguish the detail. Some other stochastic factors also take in inevitable noise into image. Great moment once go may never come back any more, which is difficult to copy. Simple but effective approach to restore image and improve the quality became urgency. Our research thus center about various forms of image blurs.Our main work and innovation include the following:1We look back at background, figure out the difference between image restoration and image enhancement. We introduce the advantage of image restoration methods refer to other restoration methods, and forsee its broad application. Concerning what is image restoration, we compare it with the notion of image enhancement. And then we elaborate the key technology difficulties in image restoration method.2Blind deconvolution of blur image is a most challenging job, the illness of which was mainly due to the number of known less than that of unknown. We set up a framework to blind deconvolution based on a single burred image, and try to add auxiliary information as condition to restrict unknowns, here comes concrete measures:two regularization cost function which involve salient edges as spatial prior to alternately iterate the blur kernel and latent image until convergence, where we can get the blur kernel. Then sparse prior is used to restore the latent image which can preserve more structure detail. In order to reduce noise in blur kernel, we put forward some revisal principle. We propose adaptive kernel size to avoid blindly manual set.3Based on respective mathematical model, we make several rapid processes on several special blur. We find blur parameters (which are motion length and motion direction) in logarithmic spectrum in linear motion blur images, and make precise evaluate combine with error-parameter. Defocus radius can be found in first circle- loop of defocus blur image cestrum. We use Hough transform to extract the edge of rotated blur image, and then the least square was applied to fit the location of the center. Thus pixels on each concentric circle were picked up to one-dimensional restoration respectively. A series of morphology methods can segment blur area from latent area in local blur images, then blind deconvolution framework was applied to restore the blur area. Then merge it with latent area can get the last deblurred image.4For noisy blur images which are more ill, we first utilize directional low-pass filter and inverse Radon transform to restructure the blur kernel. In the denoise and deconvolution stage, we construct two cost functions to denoise and deconvolution alternately, latent image can be obtained when they convergent.5We put forward a criterion of objective assessment which consider the subjective feelings for blur image evaluation. We not only concern some objective measure criteria like saturation, contrast, edge information and power spectral which based on natural statistical laws, but also effectively take in consider of image texture which sensitive to human vision, and a weighted evaluation function is made up. The least square nonlinear regress method is applied to fit the weight factor of each item.更多还原