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Image Fusion Algorithms Using Human Visual System in Transform Domain

机译:图像融合算法在变换域中使用人类视觉系统

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The endeavor of digital image fusion is to combine the important visual parts from various sources to advance the visibility eminence of the image. The fused image has a more visual quality than any source images. In this paper, the Human Visual System (HVS) weights are used in the transform domain to select appropriate information from various source images and then to attain a fused image. In this process, mainly two steps are involved. First, apply the DWT to the registered source images. Later, identify qualitative sub-bands using HVS weights. Hence, qualitative sub-bands are selected from different sources to form high quality HVS based fused image. The quality of the HVS based fused image is evaluated with general fusion metrics. The results show the superiority among the state-of- the art resolution Transforms (MRT) such as Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Contourlet Transform (CT), and Non Sub Sampled Contourlet Transform (NSCT) using maximum selection fusion rule.
机译:数字图像融合的努力是将重要的视觉零件与各种来源组合,以提高图像的可见性偏差。融合图像具有比任何源图像更高的视觉质量。在本文中,人类视觉系统(HVS)权重在变换域中用于从各种源图像中选择适当的信息,然后达到融合图像。在这个过程中,主要涉及两步。首先,将DWT应用于注册的源图像。稍后,使用HVS重量识别定性子带。因此,定性子带选自不同源以形成基于高质量的HVS的熔融图像。基于HVS的融合图像的质量进行了融合度量评估。结果显示了最先进的最新状态变换(MRT),例如离散小波变换(DWT),静止小波变换(SWT),Contourlet变换(CT)和非子采样轮廓件变换(NSCT)的优势最大选择融合规则。

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