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Adaptive weighted image fusion algorithm based on NSCT multi-scale decomposition

机译：基于NSCT多尺度分解的自适应加权图像融合算法

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摘要

The purpose of infrared image and visible light image fusion is to preserve as much as possible the target information in the infrared image and the detailed information in the visible light image. For this purpose, this paper first extracts the infrared image saliency map based on guided filtering, uses NSCT to decompose infrared and visible light images at multiple scales, adds the infrared image saliency map to the low-frequency component, and applies weighted average fusion through the adaptive infrared weight map. In the high frequency component, the absolute value of the maximum coefficient is taken to be the maximum for fusion. This method takes into account the generally high brightness of human targets in infrared images, maintains the edge information of the image, and fully considers the anti-noise performance of the fusion method, and can adaptively take into account the complex and changeable environment.
机译：红外图像和可见光图像融合的目的是尽可能保留红外图像中的目标信息和可见光图像中的详细信息。为此，本文首先基于导引滤波提取红外图像显着图，使用NSCT分解多尺度的红外和可见光图像，将红外图像显着图添加到低频分量，然后通过加权平均融合自适应红外权重图。在高频分量中，将最大系数的绝对值取为最大值以进行融合。该方法考虑了红外图像中人类目标通常较高的亮度，维护了图像的边缘信息，并充分考虑了融合方法的抗噪性能，并且可以自适应地考虑复杂多变的环境。

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