- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Generation of enhanced information image using curvelet-transform-based image fusion for improving situation awareness of observer during surveillance
摘要： Image fusion has been widely used to combine multispectral information into an enhanced information image. The application of such enhanced information content in the field of surveillance for improving situation awareness of observer is highly recommended. When a single sensor information is used for surveillance like visible camera output during poor ambient lighting conditions, ‘hot-target’ details are not visible to the observer. The use of visible-infrared fused image is recommended during surveillance in poor ambient lighting conditions to visualise background scene details and ‘hot-target’ details simultaneously. A wrapping-based curvelet transform method is proposed for fusion of infrared and visible images. Curvelet transform is used because of its advantages over wavelet transform limitations like directional insensitivity, isotropic basis and inability to resolve curves. The approximation coefficients are fused using the principal component analysis rule while detailed coefficients are fused using absolute maximum rule. The reconstructed fused image is compared with results of other fusion approaches proposed in literature. The performance of proposed wrapping-based curvelet fusion method is found visually and statistically better in comparison to other fused image outputs. The fused image obtained using proposed method retains background details as well as hot target presence with fidelity.
关键词： Curvelet transform,infrared image,situation awareness,visible image,principal component analysis,image fusion
更新于2025-09-09 09:28:46
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Effective detection by fusing visible and infrared images of targets for Unmanned Surface Vehicles
摘要： The research progress for Unmanned Surface Vehicle (USV) is of great significance to human off-shore operations. Target detection is the foundation for USV applications. Ocean wave, frog, and illumination are the most important factors that affect exactness of target detection through visible and infrared images. This paper proposes an algorithm for weighted averaging fusion of visible/infrared images. Firstly, the visible light/infrared devices are required to collect the target surrounding information, perform feature analysis, and complete the anti-fog and de-noising preprocessing. These operations aim at improving the accuracy of image segmentation. Secondly, feature extractions of the visible and infrared target images are performed, respectively, and the recognition of the target image is further completed. Finally, image fusion is performed by weighted averaging of the targets detected by visible light and infrared images. The fusion uses a matching matrix to represent the similarity of the two images. When the two images are very similar, the image is fused by weighting pixels, which effectively improves the accuracy of the detection. Lots of simulations were conducted on MATLAB 2015a with a personal computer, and the results verified the success rate of target detection and recognition.
关键词： fusion,Multi-scale fractal,target detection,visible image,infrared image
更新于2025-09-09 09:28:46
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DenseFuse: A Fusion Approach to Infrared and Visible Images
摘要： In this paper, we present a novel deep learning architecture for infrared and visible images fusion problem. In contrast to conventional convolutional networks, our encoding network is combined by convolutional layers, fusion layer and dense block in which the output of each layer is connected to every other layer. We attempt to use this architecture to get more useful features from source images in encoding process. And two fusion layers(fusion strategies) are designed to fuse these features. Finally, the fused image is reconstructed by decoder. Compared with existing fusion methods, the proposed fusion method achieves state-of-the-art performance in objective and subjective assessment.
关键词： visible image,image fusion,infrared image,dense block,deep learning
更新于2025-09-04 15:30:14