Significant target analysis and detail preserving based infrared and visible image fusion

Abstract

Infrared and visible image fusion aims to integrate the prominent infrared target and visible texture details as much as possible. However, infrared images are susceptible to noise pollution during the transmission process, which could reduce the quality of the fused image. To solve this problem, a novel significant target analysis and detail preserving based scheme is proposed for infrared and visible image fusion tasks. Since the infrared image has high visual saliency, the noise pixels in the infrared image are often retained into the fused result by most traditional fusion methods. To better highlight the infrared targets while reducing interference from noise pixels, an infrared target detection model is proposed based on boundary-aware salient target detection network (BASNet) and significant target analysis. To fully preserve the visual details, we employ a weight mean curvature (WMC) based multiscale transform (MST) fusion scheme which can effectively suppress noise and preserve valuable details. Difference of Gaussian (DoG) is also applied to enhance the overall details in the proposed fusion scheme. Qualitative and quantitative experimental results demonstrate that the proposed method can generate fused images with abundant texture details and prominent infrared targets, which is superior to some of the existing methods in visual effect and objective quality evaluations.