Abstract
The most fundamental purpose of infrared (IR) and visible (VI) image fusion is to integrate the useful information and produce a new image which has higher reliability and understandability for human or computer vision. In order to better preserve the interesting region and its corresponding detail information, a novel multiscale fusion scheme based on interesting region detection is proposed in this paper. Firstly, the MeanShift is used to detect the interesting region with the salient objects and the background region of IR and VI. Then the interesting regions are processed by the guided filter. Next, the nonsubsampled contourlet transform (NSCT) is used for background region decomposition of IR and VI to get a low-frequency and a series of high-frequency layers. An improved weighted average method based on per-pixel weighted average is used to fuse the low-frequency layer. The pulse-coupled neural network (PCNN) is used to fuse each high-frequency layer. Finally, the fused image is obtained by fusing the fused interesting region and the fused background region. Experimental results demonstrate that the proposed algorithm can integrate more background details as well as highlight the interesting region with the salient objects, which is superior to the conventional methods in objective quality evaluations and visual inspection.
Highlights
Image fusion is an important branch of information science, which has been widely used in many fields, such as bioinformatics, medical image processing, and military target visualization
We use an improved weighted average method based on per-pixel weighted average and pulse-coupled neural network (PCNN) to process the low-frequency and high-frequency layers, respectively
We improve the accuracy of interesting region detection where it contains highlighting target and heat sources
Summary
Image fusion is an important branch of information science, which has been widely used in many fields, such as bioinformatics, medical image processing, and military target visualization. Due to the excellent characteristics of the multiscale decomposition method, the MST-based method could get a good fusion effect compared with early fusion methods, such as NSCTPCNN [17]. These methods usually failed to highlight the target information in the fused image. IR image target detection-based method is another popular IR and VI image fusion method; these methods detected the target region of the IR image firstly, fused the background regions using other methods to get the fused background image, and fused the target region and background regions directly to get a new image The advantages of these methods can fully retain the infrared target information in the fused image, but commonly, these infrared target regions.
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