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基于 NSCT 域距离加权区域能量和结构张量的多聚焦图像融合。

Multi-Focus Image Fusion via Distance-Weighted Regional Energy and Structure Tensor in NSCT Domain.

机构信息

College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.

出版信息

Sensors (Basel). 2023 Jul 4;23(13):6135. doi: 10.3390/s23136135.

Abstract

In this paper, a multi-focus image fusion algorithm via the distance-weighted regional energy and structure tensor in non-subsampled contourlet transform domain is introduced. The distance-weighted regional energy-based fusion rule was used to deal with low-frequency components, and the structure tensor-based fusion rule was used to process high-frequency components; fused sub-bands were integrated with the inverse non-subsampled contourlet transform, and a fused multi-focus image was generated. We conducted a series of simulations and experiments on the multi-focus image public dataset Lytro; the experimental results of 20 sets of data show that our algorithm has significant advantages compared to advanced algorithms and that it can produce clearer and more informative multi-focus fusion images.

摘要

本文提出了一种基于非下采样轮廓变换域中距离加权区域能量和结构张量的多聚焦图像融合算法。该算法使用基于距离加权的区域能量融合规则来处理低频分量,使用基于结构张量的融合规则来处理高频分量;融合后的子带通过逆非下采样轮廓变换进行合成,生成融合后的多聚焦图像。我们在 Lytro 多聚焦图像公共数据集上进行了一系列仿真和实验,20 组数据的实验结果表明,与先进算法相比,我们的算法具有显著优势,能够生成更清晰、更具信息量的多聚焦融合图像。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df47/10346789/abdda45d1fcf/sensors-23-06135-g001.jpg

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