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• W4308695786 startingPage "1633" @default.
• W4308695786 abstract "Existing fusion rules focus on retaining detailed information in the source image, but as the thermal radiation information in infrared images is mainly characterized by pixel intensity, these fusion rules are likely to result in reduced saliency of the target in the fused image. To address this problem, we propose an infrared and visible image fusion model based on significant target enhancement, aiming to inject thermal targets from infrared images into visible images to enhance target saliency while retaining important details in visible images. First, the source image is decomposed with multi-level Gaussian curvature filtering to obtain background information with high spatial resolution. Second, the large-scale layers are fused using ResNet50 and maximizing weights based on the average operator to improve detail retention. Finally, the base layers are fused by incorporating a new salient target detection method. The subjective and objective experimental results on TNO and MSRS datasets demonstrate that our method achieves better results compared to other traditional and deep learning-based methods." @default.
• W4308695786 created "2022-11-14" @default.
• W4308695786 creator A5054513645 @default.
• W4308695786 creator A5055119733 @default.
• W4308695786 creator A5086830713 @default.
• W4308695786 date "2022-11-10" @default.
• W4308695786 modified "2023-09-25" @default.
• W4308695786 title "Infrared and Visible Image Fusion with Significant Target Enhancement" @default.
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• W4308695786 doi "https://doi.org/10.3390/e24111633" @default.
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