Infrared and visible image fusion is a process whereby infrared and visible spectral images are combined to enhance the visual quality of the resulting image and to retain more detailed information. Traditional infrared fusion techniques tend to suffer from information loss and lack of clarity of texture details. To retain more detailed information and improve image quality in infrared and visible image fusion, a method is proposed which involves extracting the base and detail layers using Gaussian filtering and anisotropic diffusion, respectively. The base layer is then fused using an average fusion strategy, the detail layer is fused based on a content-aware fusion strategy, and the processed base and detail layer images are fused into a final image. Experiments were conducted on several scenes from publicly available datasets. The experimental results demonstrate that the method proposed in this paper can generate fused images with clear targets and rich details, while simultaneously exhibiting a notable enhancement in indicators such as average gradient and spatial frequency.
A single Visible (VIS) image or Infrared (IR) image cannot clearly present the texture details and heat source information of the scene. The fusion task of IR images and VIS can combine the respective characteristics of the two types of modalities to form a new image with rich details, which can better serve advanced vision tasks. Firstly, Kmeans and connected component analysis are used to obtain the IR saliency map. Then, we use Gaussian pyramid to perform multi-scale decomposition of VIS images. Use Gabor filter to extract texture to generate VIS detail map. Finally we use the adaptive fusion rule to obtain high-quality fused images. By conducting a large number of experiments on TNO and LLVIP, and compared them with existing image fusion methods. The experimental results show that our proposed algorithm has potential performance in both quantitative and qualitative evaluations, and has real-time performance during the fusion process.
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