数字图像复原技术综述

数字图像复原的目的是将所观测到的退化图像恢复到退化前的原始图像,该恢复过程在很多图像处理应用中至关重要。近年来,图像复原技术虽已得到广泛研究,但仍有值得改进之处。为了使该领域的研究人员对当前各种图像复原方法有较全面的了解,在众多技术文献中选取了具有典型性的95篇来对各种图像复原方法进行分类综述。首先通过对复原技术问题的描述,揭示了复原技术的数学背景;其次以数字图像复原技术发展为主线,将复原方法归结为两大类进行详细讨论。一类是经典图像复原方法,另一类是现代图像复原方法。前者反应复原技术背景与发展过程,后者体现复原技术的发展趋势与面临的困难。最后,在总结全文的基础上,指出在今后进一步研究中值得关注的7项问题。     

后期模拟制作相机镜头背景模糊效果

在摄影实践中,虚化背景是突出主体的常用手段。但是由于消费级DC镜头的实际焦距都很短,因此实现浅景深而虚化背景的难度较大。如果我们希望用消费级DC也能达到虚化背景突出主体的效果,那么,Photoshop后期处理可以派上用场。     

基于差分进化的置换混叠图像盲分离

针对1类存在模糊差异的置换混叠图像,提出了1种基于差分进化的置换混叠图像盲分离算法。选择空域作为特征域,将置换混叠图像的梯度图像分成小块,并为每个小块设定个阈值,组成1个阈值向量。利用差分进化算法获得最优的阈值向量,将置换混叠图像阈值化为二值图像,利用图像形态学实现二值图像分离出置换图像。实验结果表明,与阈值法相比较,本算法能较好地把置换图像从置换混叠图像中分离出来,而不受置换图像位置、大小和个数的限制。     

一种大尺度Gauss模糊的快速采样算法

针对大模板Gauss模糊的传统算法计算复杂度较高的缺点,提出了一种基于快速采样的新算法,将计算复杂度从O(N2)降到O(N)级别.利用极大似然估计理论,对传统Gauss模板和采样模板进行分析,给出了采样算法和传统算法的联系与区别.实验表明,新算法在极大的提高算法的速度的同时,可以将两者之间的能量误差控制在1%左右,当采样点等于10N时,人眼基本无法察觉两种算法的差异.     

部分模糊核已知的混合模糊图像复原算法

真实环境中得到的图像常会受到多种模糊降质过程的影响,导致成像质量下降,为此提出一种图像退化模型及相应的复原算法.与传统的级联方式不同,该算法假定模糊核函数是散焦与运动模糊的加权和形式;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,采用文中的复原算法能够较为准确地辨识出模糊核,提升了图像复原的效果.     

一种新的运动模糊图像恢复方法

通过对运动模糊产生原因的分析,提出了一种去运动模糊的新方法。首先应用Hough变换和自相关函数估计出运动模糊的方向和长度,然后应用迭代步长自适应的整体变分模型进行图像恢复。实验结果表明,这样的空间域处理方法,不但可以避免传统的频率域去模糊方法产生的震铃效应,而且该方法具有良好的抗噪性和对运动模糊参数估计误差的低敏感性。     

Scanning Depth of Route Panorama Based on Stationary Blur

This work achieves an efficient acquisition of scenes and their depths along long streets. A camera is mounted on a vehicle moving along a straight or a mildly curved path and a sampling line properly set in the camera frame scans the 1D images over scenes continuously to form a 2D route panorama. This paper proposes a method to estimate the depth from the camera path by analyzing a phenomenon called stationary blur in the route panorama. This temporal blur is a perspective effect in parallel projection yielded from the sampling slit with a physical width. We analyze the behavior of the stationary blur with respect to the scene depth, vehicle path, and camera properties. Based on that, we develop an adaptive filter to evaluate the degree of the blur for depth estimation, which avoids error-prone feature matching or tracking in capturing complex street scenes and facilitates real time sensing. The method also uses much less data than the structure from motion approach so that it can extend the sensing area significantly. The resulting route panorama with depth information is useful for urban visualization, monitoring, navigation, and modeling.     

Are Edges Incomplete?

We address the problem of computing a general-purpose early visual representation that satisfies two criteria. 1) Explicitness: To be more useful than the original pixel array, the representation must take a significant step toward making important image structure explicit. 2) Completeness: To support a diverse set of high-level tasks, the representation must not discard information of potential perceptual relevance. The most prevalent representation in image processing and computer vision that satisfies the completeness criterion is the wavelet code. In this paper, we propose a very different code which represents the location of each edge and the magnitude and blur scale of the underlying intensity change. By making edge structure explicit, we argue that this representation better satisfies the first criterion than do wavelet codes. To address the second criterion, we study the question of how much visual information is lost in the representation. We report a novel method for inverting the edge code to reconstruct a perceptually accurate estimate of the original image, and thus demonstrate that the proposed representation embodies virtually all of the perceptually relevant information contained in a natural image. This result bears on recent claims that edge representations do not contain all of the information needed for higher level tasks.     

SAR图像运动模糊参数估计

本文针对SAR图像运动模糊的特点,提出了一种自适应滤波方法,结合统计平均思想,利用自相关的方法成功地提取出运动模糊的点扩展函数的长度,并把这个长度作为SAR图像运动模糊程度的度量.     

运动模糊不变量研究

图像的运动模糊是摄像机在曝光期间与场景间相对运动而造成的图像退化,但现有算法对运动模糊的恢复精度有限.本文分析了图像运动模糊的退化模型,提出了运动模糊不变量的概念,证明了图像零阶矩、第一个Hu不变矩和SIFT为运动模糊不变量,并给出了测试和应用.结果表明运动模糊不变量是存在的,而且可以用于不确定性运动模糊图像的匹配和识别领域.