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A novel multi-focus image fusion method based on distributed compressed sensing

机译：一种基于分布式压缩感测的新型多焦型图像融合方法

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Multi-focus image fusion aims to produce an all-in-focus image by merging multiple partially focused images of the same scene. The main work is identifying the focused region and then composing all the focused regions. In this paper, a novel efficient multi-focus image fusion method based on distributed compressed sensing (DCS) is proposed. Firstly, the low-frequency and high-frequency images are obtained by comparing the variance of the source images, which are further utilized to get the low-frequency and high-frequency dictionaries. Secondly, DCS using joint sparsity model-1 (JSM-1 ) is applied to reconstruct the precise high-frequency images. Thirdly, the decision map is obtained based on all the high-frequency images and then improved by the morphological processing. Finally, the focused pixels are chosen from the source images through the decision map. Experimental results indicate that the proposed DCS-based method can be competitive with or even outperform some state-of-the-art methods in terms of both visual and quantitative metric evaluations. (C) 2020 Elsevier Inc. All rights reserved.
机译：多聚焦图像融合旨在通过合并相同场景的多个部分聚焦的图像来产生全焦焦图像。主要工作是识别聚焦区域，然后构成所有聚焦区域。本文提出了一种基于分布式压缩检测（DCS）的新型有效的多聚焦图像融合方法。首先，通过比较进一步利用的源图像的方差来获得低频和高频图像，这进一步利用了用于获得低频和高频词典。其次，应用了使用关节稀疏模型-1（JSM-1）的DC来重建精确的高频图像。第三，基于所有高频图像获得决策地图，然后通过形态处理改善。最后，通过决定图从源图像中选择聚焦的像素。实验结果表明，在视觉和定量度量评估方面，所提出的基于DCS的方法可以竞争或甚至优于某些最先进的方法。 （c）2020 Elsevier Inc.保留所有权利。

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