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Simultaneous Image Fusion and Denoising based on Multi-Scale Transform and Sparse Representation

机译:基于多尺度变换和稀疏表示的同时图像融合与去噪

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Multi-scale transform (MST) and sparse representation (SR) techniques are used in an image representation model. Image fusion is used especially in medical, military and remote sensing areas for high resolution vision. In this paper an image fusion technique based on shearlet transformation and sparse representation is proposed to overcome the natural defects of both MST and SR based methods. The proposed method is also used in different transformations and SR for comparison purposes. This research also investigate denoising techniques with additive white Gaussian noise into source images and perform threshold for de-noised into the proposed method. The image quality assessments for the fused image are used for the performance of proposed method and compared with others.
机译:在图像表示模型中使用了多尺度变换(MST)和稀疏表示(SR)技术。图像融合特别用于医疗,军事和遥感领域,以实现高分辨率视觉。本文提出了一种基于剪切波变换和稀疏表示的图像融合技术,以克服基于MST和SR的方法的自然缺陷。所提出的方法还用于不同的转换和SR中,以进行比较。这项研究还研究了将加性高斯白噪声加到源图像中的去噪技术,并对所提出的方法进行去噪的阈值。对融合图像的图像质量评估可用于所提出方法的性能,并与其他方法进行比较。

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