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Classification-Based Adaptive Filtering for Multiframe Blind Image Restoration

机译:基于分类的自适应滤波用于多帧盲图像恢复

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摘要

In this paper, the blind restoration of a scene is investigated, when multiple degraded (blurred and noisy) acquisitions are available. An adaptive filtering technique is proposed, where the distorted images are filtered, classified and then fused based upon the classification decisions. Finite normal-density mixture (FNM) models are used to model the filtered outputs at each iteration. For simplicity, fixed number of Gaussian components (classes) is, initially, considered for each degraded frame and the selection of the optimal number of classes is performed according to the global relative entropy criterion. However, there exist cases where dynamically varying FNM models should be considered, where the optimal number of classes is selected according to the Akaike information criterion. The iterative application of classification and fusion, followed by optimal adaptive filtering, converges to a global enhanced representation of the original scene in only a few iterations. The proposed restoration method does not require knowledge of the point-spread-function support size or exact alignment of the acquired frames. Simulation results on synthetic and real data, using both fixed and dynamically varying FNM models, demonstrate its efficiency under both noisy and noise-free conditions.
机译:在本文中,当多个降级(模糊和嘈杂)的采集可用时,将研究场景的盲恢复。提出了一种自适应滤波技术,其中,基于分类决策对失真图像进行滤波,分类和融合。有限的正常密度混合(FNM)模型用于在每次迭代时对滤波后的输出进行建模。为简单起见,首先为每个降级帧考虑固定数量的高斯分量(类),然后根据全局相对熵准则执行最佳类数的选择。但是,在某些情况下,应考虑动态变化的FNM模型,根据Akaike信息准则选择最佳类别数。分类和融合的迭代应用,然后是最佳自适应滤波,仅需几次迭代即可收敛到原始场景的全局增强表示。所提出的恢复方法不需要点扩展功能支持大小的知识或所获取帧的精确对准。使用固定和动态变化的FNM模型对合成和真实数据进行的仿真结果证明了其在嘈杂和无噪声条件下的效率。

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