Iterative statistical approach to blind image deconvolution
Iterative statistical approach to blind image deconvolution
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DOI：
10.1364/josaa.17.001177
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发表时间：
2000-07-01
影响因子：
1.9
通讯作者：
Goodman, JW
中科院分区：
文献类型：
--
作者：
Lam, EY;Goodman, JW
图像去模糊长期以来一直被建模为反卷积问题。在文献中，通常假设点扩散函数 (PSF) 是已知的。然而，在实际情况中，例如相机中的图像采集，我们对PSF的了解可能并不完整。这种去模糊问题被称为盲去卷积。我们采用数据的统计观点，并使用修改后的最大后验方法来识别最可能的对象并在给定观察到的图像的情况下进行模糊处理。为了便于计算，我们使用迭代方法，它是传统期望最大化方法的扩展，而不是直接优化。我们推导出每次迭代中估计更新的单独公式，以增强反卷积结果，这是基于我们关于对象和模糊的先验知识的具体性质。 (C) 2000 美国光学协会 [S0740-3232(00)00507-X] OCIS 代码：100.1830、100.3020、100.2000、000.5490、110.5200。
Image deblurring has long been modeled as a deconvolution problem. In the literature, the point-spread function (PSF) is often assumed to be known exactly. However, in practical situations such as image acquisition in cameras, we may have incomplete knowledge of the PSF. This deblurring problem is referred to as blind deconvolution. We employ a statistical point of view of the data and use a modified maximum a posteriori approach to identify the most probable object and blur given the observed image. To facilitate computation we use an iterative method, which is an extension of the traditional expectation-maximization method, instead of direct optimization. We derive separate formulas for the updates of the estimates in each iteration to enhance the deconvolution results, which are based on the specific nature of our a priori knowledge available about the object and the blur. (C) 2000 Optical Society of America [S0740-3232(00)00507-X] OCIS codes: 100.1830, 100.3020, 100.2000, 000.5490, 110.5200.