Abstract
In this paper, a two-scale transform-based fusion model for visible and infrared images is proposed to integrate thermal target regions from infrared images and preserve pleasing background details from visible images. To reduce the dependency on multi-scale transform (MST) and improve efficiency, an average filter is initially used to decompose source images into a low-frequency sub-band retaining fine-scale features in small details and a high-frequency sub-band preserving high-contrast variation in intensity. A saliency detection fusion rule based on average filter and median filter is applied on high-frequency fusion to generate saliency maps. After obtaining the saliency maps, the strategy of Weighted Least Square optimization (WLS) is used to supplement visual details and reduce redundancy information to get the final fused detail layer. Meanwhile, we propose a low-frequency fusion rule, a saliency detection algorithm based on infrared thermal information enhancement and visible background detail conservation, which has good performance on maintaining brightness in infrared images and is able to capture background information in visible images. Finally, the fused image shows superior performance compared with nine advanced fusion algorithms in whether subjective or objective evaluations. Quantitative and qualitative experiments on 31 pairs of source images demonstrates that the image fused by the proposed method has abundant texture details, highlighted target regions, and high-quality non-target areas.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call