Infrared and Visible Image Fusion Based on Dual-Kernel Side Window Filtering and S-Shaped Curve Transformation

The fusion of infrared and visible images combines the advantages of thermal radiation information in infrared images and texture information in visible images. To preserve salient targets and obtain better fusion results, this article proposes a novel infrared and visible image fusion method based on dual-kernel side window filtering and detail optimization with S-shaped curve transformation. First, a dual-kernel side window box filter (DSWBF) with adaptive filter kernel size is designed to extract the base and detail layers of the source images. Then, a saliency-based fusion rule is proposed for the base layers to highlight the salient regions of the infrared image. Next, a detail optimization module is constructed based on an S-shaped curve transformation and guided filter to optimize the detail layer of the infrared image. The optimized detail layer is then merged with the detail layer of the visible image to obtain the fused detail layer. Finally, the fusion image is created by reconstructing the fused base and detail layers. Experimental results on the two public datasets indicate that compared with the state-of-the-art methods, the proposed method can obtain better fusion performance in terms of subjective and objective evaluations.

I. Introduction

Visible images clearly reflect the details and background of a scene. However, in some cases, such as under low light, fog, and other environmental conditions, targets are difficult to observe in visible images [1]. In contrast, infrared images reflect the difference in intensity between the targets and the background; in other words, they describe the difference in the thermal radiation of the targets and that of the background. However, infrared images lack detail [2]. Infrared and visible image fusion techniques are important for combining the complementary information of infrared and visible images, which is a part of multisensor information fusion and has been widely applied for purposes such as object recognition [3], remote sensing, and military reconnaissance [4].