Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130000, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2022 Aug 25;22(17):6390. doi: 10.3390/s22176390.
The purpose of infrared and visible image fusion is to generate images with prominent targets and rich information which provides the basis for target detection and recognition. Among the existing image fusion methods, the traditional method is easy to produce artifacts, and the information of the visible target and texture details are not fully preserved, especially for the image fusion under dark scenes and smoke conditions. Therefore, an infrared and visible image fusion method is proposed based on visual saliency image and image contrast enhancement processing. Aiming at the problem that low image contrast brings difficulty to fusion, an improved gamma correction and local mean method is used to enhance the input image contrast. To suppress artifacts that are prone to occur in the process of image fusion, a differential rolling guidance filter (DRGF) method is adopted to decompose the input image into the basic layer and the detail layer. Compared with the traditional multi-scale decomposition method, this method can retain specific edge information and reduce the occurrence of artifacts. In order to solve the problem that the salient object of the fused image is not prominent and the texture detail information is not fully preserved, the salient map extraction method is used to extract the infrared image salient map to guide the fusion image target weight, and on the other hand, it is used to control the fusion weight of the basic layer to improve the shortcomings of the traditional 'average' fusion method to weaken the contrast information. In addition, a method based on pixel intensity and gradient is proposed to fuse the detail layer and retain the edge and detail information to the greatest extent. Experimental results show that the proposed method is superior to other fusion algorithms in both subjective and objective aspects.
红外与可见光图像融合的目的是生成具有突出目标和丰富信息的图像，为目标检测和识别提供基础。在现有的图像融合方法中，传统方法容易产生伪影，并且不能充分保留可见目标和纹理细节的信息，特别是在暗场和烟雾条件下的图像融合。因此，提出了一种基于视觉显著性图像和图像对比度增强处理的红外与可见光图像融合方法。针对低图像对比度给融合带来困难的问题，采用改进的伽马校正和局部均值方法增强输入图像对比度。为了抑制图像融合过程中容易出现的伪影，采用差分滚动引导滤波器（DRGF）方法将输入图像分解为基础层和细节层。与传统的多尺度分解方法相比，该方法可以保留特定的边缘信息，减少伪影的发生。为了解决融合图像中显著目标不突出、纹理细节信息不充分的问题，采用显著图提取方法提取红外图像显著图来引导融合图像目标权重，另一方面用于控制基础层的融合权重，以改善传统“平均”融合方法的缺点，增强对比度信息。此外，提出了一种基于像素强度和梯度的方法来融合细节层，并最大限度地保留边缘和细节信息。实验结果表明，该方法在主观和客观方面均优于其他融合算法。
