Image fusion integrates complex information about a target scene from multiple sensors into a single image. The fused image can further be utilized for human perception or different machine vision tasks. In the case of infrared and visible images, infrared images have the advantage of capturing thermal radiation intensity, whereas visible images are superior in gradient texture. In order to effectively fuse thermal intensity of infrared image and texture advantage of visible image, we propose a novel fusion method based on L0 decomposition and intensity mask. The proposed method first acquires base and detail layers of images (visible & infrared) using L0 decomposition. Next, an intensity mask is obtained using the basic global thresholding method on base layers of infrared image. The layers (base layers and detail layers) and visible images are divided images into three parts by the use of intensity mask, namely, mask-base layers, mask-detail layers, and texture-background. The first and second parts effectively achieve intensity blending, whereas the third part achieves the fused image with a clear gradient texture. The proposed method shows superior performance when compared with five state-of-the-art methods (on publicly available databases).

中文翻译：

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通过 L0 分解和强度掩模的红外和可见光图像融合

图像融合将来自多个传感器的关于目标场景的复杂信息集成到单个图像中。融合图像还可用于人类感知或不同的机器视觉任务。在红外和可见光图像的情况下，红外图像具有捕捉热辐射强度的优势，而可见光图像在梯度纹理方面更胜一筹。为了有效融合红外图像的热强度和可见光图像的纹理优势，我们提出了一种基于L0分解和强度掩模的融合方法。所提出的方法首先使用 L0 分解获取图像的基础层和细节层（可见光和红外）。接下来，使用基本全局阈值方法在红外图像的基层上获得强度掩模。图层（基础层和细节层）和可见图像通过强度蒙版将图像分为三个部分，即蒙版基础层、蒙版细节层和纹理背景。第一部分和第二部分有效地实现了强度混合，而第三部分实现了具有清晰渐变纹理的融合图像。与五种最先进的方法（在公开可用的数据库上）相比，所提出的方法显示出优越的性能。