Simultaneous Destriping and Image Denoising Using a Nonparametric Model With the EM Algorithm

估计员 算法 数学 非本地手段 先验概率 图像(数学) 图像复原 降噪 人工智能 计算机科学 图像处理 统计 图像去噪 贝叶斯概率
作者
Lingfei Song,Hua Huang
出处
期刊:IEEE transactions on image processing [Institute of Electrical and Electronics Engineers]
卷期号:32: 1065-1077 被引量:7
标识
DOI:10.1109/tip.2023.3239193
摘要

Digital images often suffer from the common problem of stripe noise due to the inconsistent bias of each column. The existence of the stripe poses much more difficulties on image denoising since it requires another n parameters, where n is the width of the image, to characterize the total interference of the observed image. This paper proposes a novel EM-based framework for simultaneous stripe estimation and image denoising. The great benefit of the proposed framework is that it splits the overall destriping and denoising problem into two independent sub-problems, i.e., calculating the conditional expectation of the true image given the observation and the estimated stripe from the last round of iteration, and estimating the column means of the residual image, such that a Maximum Likelihood Estimation (MLE) is guaranteed and it does not require any explicit parametric modeling of image priors. The calculation of the conditional expectation is the key, here we choose a modified Non-Local Means algorithm to calculate the conditional expectation because it has been proven to be a consistent estimator under some conditions. Besides, if we relax the consistency requirement, the conditional expectation could be interpreted as a general image denoiser. Therefore other state-of-the-art image denoising algorithms have the potentials to be incorporated into the proposed framework. Extensive experiments have demonstrated the superior performance of the proposed algorithm and provide some promising results that motivate future research on the EM-based destriping and denoising framework.
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