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首页> 外文会议>International conference on graphic and image processing >Multi-focus Image Fusion Based on Area-Based Standard Deviation in Dual Tree Contourlet Transform Domain
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Multi-focus Image Fusion Based on Area-Based Standard Deviation in Dual Tree Contourlet Transform Domain

机译：双树轮廓波变换域中基于区域标准差的多焦点图像融合

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摘要

Multiresolution-based methods, such as wavelet and Contourlet are usually used to image fusion. This work presents a new image fusion frame-work by utilizing area-based standard deviation in dual tree Contourlet trans-form domain. Firstly, the pre-registered source images are decomposed with dual tree Contourlet transform; low-pass and high-pass coefficients are obtained. Then, the low-pass bands are fused with weighted average based on area standard deviation rather than the simple "averaging" rule. While the high-pass bands are merged with the "max-absolute' fusion rule. Finally, the modified low-pass and high-pass coefficients are used to reconstruct the final fused image. The major advantage of the proposed fusion method over conventional fusion is the approximately shift invariance and multidirectional selectivity of dual tree Contourlet transform. The proposed method is compared with wavelet- , Contourlet-based methods and other the state-of-the art methods on common used multi focus images. Experiments demonstrate that the proposed fusion framework is feasible and effective, and it performs better in both subjective and objective evaluation.
机译：基于多分辨率的方法（例如小波和Contourlet）通常用于图像融合。这项工作通过利用双树Contourlet变换域中基于区域的标准偏差，提出了一种新的图像融合框架。首先，利用二叉树Contourlet变换对预先注册的源图像进行分解。获得低通和高通系数。然后，将低通频带与基于面积标准偏差而不是简单的“平均”规则的加权平均值相融合。在将高通频带与“最大绝对”融合规则合并时，最后，使用修改后的低通和高通系数来重构最终的融合图像，与传统融合相比，该融合方法的主要优势是对偶树Contourlet变换的近似平移不变性和多方向选择性，将该方法与基于小波，Contourlet的方法和其他最新方法在常用的多焦点图像上进行了比较，实验证明了该融合方法的有效性。该框架是可行和有效的，并且在主观和客观评估方面都有较好的表现。

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