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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Cartoon-Texture Decomposition-Based Variational Pansharpening
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Cartoon-Texture Decomposition-Based Variational Pansharpening

机译：卡通纹理分解的变分泛粉虱

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摘要

Pansharpening is widely used to increase the spatial resolution of a multispectral (MS) image by fusing with a panchromatic (PAN) image that has high-spatial resolution and the same scene. In this paper, the similarities of MS and PAN images in cartoon-texture space are exploited. The cartoon and texture components of an image always contain the global structure information and the locally-patterned information, respectively. Therefore, the global and local spatial details (i.e., high-order information) could be preserved well in the fused high-spatial resolution MS image after leveraging the similarities of these images. Pansharpening is formulated as the optimization problem with respect to the cartoon-texture similarities between the MS and the PAN images in this work based on the aforementioned observation. Specifically, cartoon similarity is determined through gradient sparsity and formulated as a total variation term, whereas texture similarity is described according to the low-rank property. The alternative direction multiplier method is used to solve the optimization problem. In the experiment, the Gaofen-1 satellite dataset is used to compare the proposed method with other classical pansharpening methods. Experimental results demonstrate that our method outperforms the comparison methods in terms of visual and quantitative qualities.
机译：Pansharpening广泛用于通过熔断具有高空间分辨率和相同场景的平面（PAN）图像来增加多光谱（MS）图像的空间分辨率。在本文中，利用了MS和PAN​​图像在卡通纹理空间中的相似性。图像的卡通和纹理组件分别包含全局结构信息和本地图案化信息。因此，在利用这些图像的相似性之后，可以在熔融的高空间分辨率MS图像中保持全局和局部空间细节（即，高阶信息）。基于上述观察，Pansharpening是关于MS和PAN​​图像之间的卡通纹理相似性的优化问题。具体地，通过梯度稀疏性确定卡通相似性，并配制成总变化项，而根据低秩属性描述纹理相似度。替代方向乘法器方法用于解决优化问题。在实验中，GaoFen-1卫星数据集用于比较其他经典泛散形方法的提出方法。实验结果表明，我们的方法在视觉和定量质量方面优于比较方法。

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