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[IEEE 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) - Tamilnadu, India (2019.4.11-2019.4.13)] 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) - An Efficient MRI-PET Medical Image Fusion using Non-Subsampled Shearlet Transform
摘要： Multimodal medical image fusion is an effective method to incorporate the pertinent details from a variety of medical images into a solitary image. The outcome would be more factual than any of the input source images. Here, an efficient integrative scheme based on Non- Subsampled Shearlet transform in YIQ color space is considered. The suggested approach is well in enhancing boundary points in medical image analysis, data conveying points utilized to reveal the stronger visual framework of the image. The similar experimental outcomes and investigation make visible that the proposed strategy gives more enhanced results with reference to some assessment measures. Our proposed method can improve the information content, visual quality and the edge information simultaneously.
关键词： Average Combination Rule,Curvelet Transform,Discrete Wavelet Transform,Non-Subsampled Shearlet Transform,Choose Max Fusion Rule,Undecimated Wavelet Transform
更新于2025-09-16 10:30:52
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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