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Fusion of panchromatic image with multi-spectral image using robust adaptive normalized convolution

机译:使用鲁棒自适应标准化卷积的多光谱图像融合了全谱图像

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This paper proposes a novel technique for reconstruction of a high resolution (HR) image by fussing irregularly sampled data of panchromatic image and multi-spectral image. The idea behind the fusion process is normalized convolution which uses the technique of approximating the local signals by projecting on to subspace. The normalized convolution used in this paper contains the polynomial basis functions similar to that of a Taylor series expansion. The window function used in the adaptive normalized convolution helps in improving the analysis by grouping more samples of same sense. This in tum helps to escalate the signal to noise ratio and curtail the diffusion across discontinuities. The necessity of multi-sensor data in required in many fields such as defense, remote-sensing, machine vision, medical imaging and the proposed method supplied promising results to such applications. (C) 2019 Elsevier B.V. All rights reserved.
机译:本文提出了一种通过阐明的全形图像和多光谱图像的不规则采样数据来重建高分辨率(HR)图像的新技术。 融合过程背后的想法是标准化的卷积,它通过将本地信号突出到子空间来使用近似局部信号的技术。 本文中使用的标准化卷积包含与泰勒序列扩展类似的多项式基础函数。 在自适应归一化卷积中使用的窗口功能有助于通过分组更多的相同意义的样本来提高分析。 这在肿瘤中有助于将信号升级到噪声比率,并在不连续性上缩减扩散。 在许多领域所需的多传感器数据的必要性,例如防御,遥感,机器视觉,医学成像和所提出的方法向这些应用提供了有希望的结果。 (c)2019年Elsevier B.V.保留所有权利。

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