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Multimodal sensor medical image fusion based on mutual- structure for joint filtering using sparse representation

机译:基于互结构的稀疏表示基于联合结构的多模态传感器医学图像融合

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Multimodal sensor medical image fusion has been widely reported in recent years, but the fused image by the existing methods introduces low contrast information and little detail information. To overcome this problem, the new image fusion method is proposed based on mutual-structure for joint filtering and sparse representation in this article. First, the source image is decomposed into a series of detail images and coarse images by mutual-structure for joint filtering. Second, sparse representation is adopted to fuse coarse images and then local contrast is applied for fusing detail images. Finally, the fused image is reconstructed by the addition of the fused coarse images and the fused detail images. By experimental results, the proposed method shows the best performance on preserving detail information and contrast information in the views of subjective and objective evaluations.
机译:近年来,多模式传感器医学图像融合已被广泛报道,但是现有方法的融合图像引入了低对比度信息和很少的细节信息。为了克服这个问题,本文提出了一种基于互结构的图像融合方法,用于联合滤波和稀疏表示。首先,通过相互结构将源图像分解为一系列的细节图像和粗糙图像,以进行联合滤波。其次,采用稀疏表示法融合粗糙图像,然后应用局部对比度融合细节图像。最后,通过将融合的粗糙图像和融合的细节图像相加来重构融合图像。通过实验结果,从主观和客观评价的角度来看,该方法在保留细节信息和对比度信息方面表现出最好的性能。

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