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首页> 外文期刊>International journal of image and data fusion >Singular value decomposition-based anisotropic diffusion for fusion of infrared and visible images
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Singular value decomposition-based anisotropic diffusion for fusion of infrared and visible images

机译：基于奇异值分解的各向异性扩散，用于红外和可见光图像的融合

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摘要

Image fusion is an important part of image processing because it can collaboratively use image information from different sensors for the same scene. Image fusion outputs the images, which are more suitable for human visual perception or computer processing and analysis; furthermore, it can obviously improve the shortcomings of a single sensor, as well as improve the image definition and packet content, making it conducive to accurately, reliably and comprehensively obtaining information regarding the target or scenarios. To improve the fusion precision of infrared and visible images, a new image fusion method for infrared and visible images is proposed in this paper. The proposed method is partitioned into three parts. First, anisotropic diffusion is used to decompose the source images into base and detail layers. Additionally, singular-value decomposition and principal-component analysis are adopted to calculate the final base and detail layers, respectively. Then, final detail and base layers are fused using the Fourier transform method. Finally, experiments with various infrared and visible images are conducted to evaluate the proposed method. Experimental and contrastive results show that the new algorithm has a good subjective visual effect; in addition, the objective evaluation index is improved significantly in terms of definition, mutual information and entropy.
机译：图像融合是图像处理的重要组成部分，因为它可以针对同一场景协作使用来自不同传感器的图像信息。图像融合输出的图像更适合于人类的视觉感知或计算机处理与分析；此外，它显然可以改善单个传感器的缺点，并且可以改善图像清晰度和数据包内容，从而有利于准确，可靠和全面地获取有关目标或场景的信息。为了提高红外与可见光图像的融合精度，提出了一种红外与可见光图像融合的新方法。所提出的方法分为三个部分。首先，利用各向异性扩散将源图像分解为基础层和细节层。另外，采用奇异值分解和主成分分析分别计算最终的基础层和细节层。然后，使用傅里叶变换方法将最终的细节层和基础层融合在一起。最后，利用各种红外和可见光图像进行实验，以评估该方法。实验和对比结果表明，该算法具有良好的主观视觉效果。此外，客观评价指标在清晰度，互信息和熵方面都有显着提高。

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