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Image identification and restoration in the subband domain

机译:子带域中的图像识别和恢复

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摘要

When faced with a large support point spread function (PSF), the iterative expectation-maximization (EM) algorithm, which is often used for PSF identification, is very sensitive to the initial PSF estimate. To deal with this problem, the authors propose to do EM image identification and restoration in the subband domain. After the image is first divided into subbands, the EM algorithm is applied to each subband separately. Since the PSF can be taken to have smaller support in each subband, these subbands should be less of a problem with the EM model identification. They also introduce an adaptive subband EM method for use in the upper frequency subbands.
机译:当面对较大的支持点扩展函数(PSF)时,经常用于PSF识别的迭代期望最大化(EM)算法对初始PSF估计非常敏感。为了解决这个问题,作者建议在子带域中进行EM图像识别和恢复。在将图像首先划分为子带之后,将EM算法分别应用于每个子带。由于PSF可以在每个子带中获得较小的支持,因此这些子带在EM模型识别中应该不成问题。他们还介绍了一种适用于高频子带的自适应子带EM方法。

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