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Reconstruction of images with exemplar based image inpainting and patch propagation

机译:使用基于示例的图像修复和补丁传播重建图像

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When we consider the digital images then there is strong possibility of images may get corrupted so recovery of partially damage, corrupted and modified image plays very important role in Digital Image processing. The technique which used to perform this recovering operation is known as image inpainting. In this paper we studied and reviewed two algorithms Exemplar Base Inpainting and Progressive Image Inpainting based on Wavelet Transform. These algorithms consider the surrounding information of image to get the patch based technique for completing missing regions to recover original image. To found the patch we come to know the approach, which is extension of progressive adaptive method for patch propagation. This Algorithm will help out to find patch. In this paper, two techniques are proposed to enhance the performance of Criminisis algorithm which is algorithm to inpaint image with exemplar-based approach. First, to reduce the computational complexity of global searching a gradient-based searching is developed, which drastically lowers the complexity value. Second, the patch matching process is modified with a distance-dependent criterion, such that the accuracy of the best matching candidate is enhanced.
机译:当我们考虑数字图像时,图像很有可能会被损坏,因此部分损坏,损坏和修改后的图像的恢复在数字图像处理中起着非常重要的作用。用于执行此恢复操作的技术称为图像修复。在本文中,我们研究并回顾了基于小波变换的样例基础修复和渐进式图像修复两种算法。这些算法考虑图像的周围信息以获得基于补丁的技术,以完成缺失区域以恢复原始图像。为了找到补丁,我们知道了该方法,它是用于补丁传播的渐进自适应方法的扩展。该算法将帮助您找到补丁。本文提出了两种技术来提高Criminisis算法的性能,该算法是一种基于示例方法的图像修复算法。首先,为了降低全局搜索的计算复杂度,开发了一种基于梯度的搜索,该搜索大大降低了复杂度值。其次,使用距离相关的准则修改补丁匹配过程,从而提高最佳匹配候选者的准确性。

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