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Multiscale feature learning and attention mechanism for infrared and visible image fusion

Science China(Technological Sciences) 2024年第2期67卷 408-422页

作者： GAO Li LUO DeLin WANG Song School of Aerospace Engineering Xiamen UniversityXiamen 361102China Electric Power Research Institute China Southern Power GridGuangzhou 510063China

Current fusion methods for infrared and visible images tend to extract features at a single scale,which results in insufficient detail and incomplete feature *** address these issues,we propose an infrared and visible image fusion network based on a multiscale feature learning and attention mechanism(MsAFusion).A multiscale dilation convolution framework is employed to capture image features across various scales and broaden the perceptual ***,an attention network is introduced to enhance the focus on salient targets in infrared images and detailed textures in visible *** compensate for information loss during convolution,jump connections are utilized during the image reconstruction *** fusion process utilizes a combined loss function consisting of pixel loss and gradient loss for unsupervised fusion of infrared and visible *** experiments on the dataset of electricity facilities demonstrate that our proposed method outperforms nine state-of-theart methods in terms of visual perception and four objective evaluation metrics.

关键词： infrared and visible images image fusion attention mechanism CNN feature extraction

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Fusion of infrared and visible Image Based on HIS and Wavelet Transform

Fusion of Infrared and Visible Image Based on HIS and Wavele...

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第30届中国控制与决策会议

作者： Chengtao Cai Xin Ding Harbin Engineering University

Due to the different imaging principle of infrared image and visible image, the information characteristics of the two images are different. Fusion technology is the organic fusion of images from two different sensors, with complementary information. In this paper, a novel algorithm is proposed for the fusion of infrared and visible images. The algorithm are firstly performs HIS transform on the infrared image and the visible image to obtain two sets of intensity（I）, hue（H） and saturation（S） components. Then, after the wavelet transform of the I-components, a simple fusion algorithm based on spatial domain is adopted for low frequency sub-band, and the high-frequency sub-bands adopt the fusion algorithm based on window-based average gradient. Finally, the fused I-component was wavelet reconstructed and transformed by the inverse HIS transform with S-component and H-component of visible image to obtain a fused image. Experiments show that the novel fusion algorithm of infrared and visible image proposed in this paper can preserve the background information and texture information of the visible image better when the object is highlighted in the infrared image, the fused image is clearer, and the fusion effect is better.

关键词： infrared and visible images HIS Transform Wavelet Transform Image Fusion

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