基于显著性检测与权重映射的可见光与红外图像融合算法

为了解决当前可见光(VI)与红外(IR)图像融合中易出现信息丢失,以及存在伪影等问题,提出了一种显著性检测耦合权重映射结合的VI与IR图像融合算法。分别对VI和IR图像进行二尺度分解,得到基础层和细节层。为了提高VI和IR融合效果,定义了一种显著性特征检测方法,在每个源图像上应用均值滤波以减小像素与其相邻像素之间的强度变化,以完成平滑处理。再借助中值滤波来消除每个源图像中的噪声与伪影,较好地保留图像边缘。并通过取均值和中值滤波输出的差异来计算图像的显著性特征,以突出边缘和直线等显著性信息。然后,通过对显著性检测结果完成归一化处理,以构建权重映射,为细节层分配合适的权重。再将基础层和细节层采用不同的规则进行融合,其中,联合权重映射与细节层,得到最终的细节信息,并借助平均融合规则来完成基础层的融合。最后,利用最终基础层和细节层的线性组合来构造新图像。实验表明,与当前多尺度图像融合技术相比,所提算法仅采用两尺度分解,显著提高了融合效率,而且得到的融合图像具有更好融合质量,有效消除了伪影,在融合过程中的信息丢失量更少。

Visible and infrared image fusion algorithm based on significance detection and weight mapping

In order to solve the problems of information loss and artifact in the fusion of visible light and infrared image, the image fusion algorithm of VI and IR based on saliency detection coupled weight mapping is proposed. The VI and IR images were decomposed into two scales to obtain the base layer and detail layer. In order to improve the fusion effect of VI and IR, a salient feature detection method was defined, and the mean filter was applied to each source image to reduce the intensity change between the pixel and its adjacent pixels for finishing the smoothing process. Then the median filter was applied to each source image to eliminate noise or artifacts for preserving the edges. By taking the difference between the mean value and the median filter output to calculate the significance characteristics, the salient information such as edges and lines is highlighted. Then, the significance detection results are normalized to construct the weight mapping for assigning the appropriate weight to the detail layer. And the basic layer and detail layer are fused by different rules, in which the weight map and detail layer are combined to get the final detail information, and the basic layer is fused by the average fusion rules. Finally, the linear combination of the final base layer and detail layer were used to construct the new image. Experimental results show that compared with the current multi scale image fusion technology, the proposed algorithm only uses two scale decomposition, which significantly improves the fusion efficiency, and the fusion image has better fusion quality, which effectively eliminates the artifacts and less information loss in the fusion process.