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Exemplar-based image inpainting using adaptive two-stage structure-tensor based priority function and nonlocal filtering

机译:基于样本的图像修复,使用基于自适应两阶段结构张量的优先级函数和非局部滤波

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摘要

For the exemplar-based image inpainting problem, the filling order and local intensity smoothness are two crucial factors that should be considered carefully. This work gives a new exemplar-based image inpainting method, preventing geometric structures from being destroyed and reconstructing textures well to obtain elegant-looking outputs. For a better filling order, we define a new adaptive two-stage structure-tensor based priority function. To promote the local intensity smoothness, we adopt a non-local way, and at the same time, propose a weighted filter based on a Gaussian-like function to generate the ideal filling patch by combining non-local patches. We compare the proposed method with some recent state-of-the-art image inpainting approaches on different tasks, such as texture and structure synthesis, object removal, and remote sensing images inpainting. Experimental results demonstrate the superiority of the proposed method, both visually and quantitatively.
机译:对于基于示例的图像修复问题,填充顺序和局部强度平滑度是应仔细考虑的两个关键因素。该工作提供了一种新的基于示例的图像修复方法,可以防止几何结构被破坏,并很好地重建纹理以获得优雅的输出。为了获得更好的填充顺序,我们定义了一种新的基于自适应两阶段结构张量的优先级函数。为了提高局部强度的平滑度,我们采用非局部的方式,同时提出了一种基于类高斯函数的加权滤波,通过结合非局部面片来生成理想的填充面片。我们将所提出的方法与一些最新最先进的图像修复方法进行了比较,例如纹理和结构合成、物体去除和遥感图像修复。实验结果从视觉和定量两个方面验证了所提方法的优越性。

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