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METHOD OF EXEMPLAR-BASED IMAGE INPAINTING USING STRUCTURE MATRIX

机译:结构矩阵的基于示例图像的输入方法

摘要

The present invention relates to a method for example-based image inpainting using a structure matrix, which is capable of calculating the priority of all pixels belonging to the boundary of an area to be reconstructed in image data to be reconstructed to determine a pixel with the highest priority in the boundary of the area to be reconstructed, calculating patch similarity between the pixel with the highest priority in the boundary of the area to be reconstructed and all pixels belonging to a known area to select a pixel with the highest similarity in the known area, copying patches around the pixel with the highest similarity in the known area to patches around the pixel with the highest priority in the boundary of the area to be reconstructed to extend the known area and reduce the area to be reconstructed, repeating the above processes until all the areas to be reconstructed are removed from the image data to be reconstructed. Thus, the priority of a patch to be reconstructed can be determined by using priorities of an area obtained by using a structure matrix, a patch most similar to the patch to be reconstructed can be selected, and an image to be reconstructed can be inpainted to reconstruct the image more naturally, thereby significantly improving the performance of image inpainting. [Reference numerals] (AA) Start; (BB) No; (CC) Yes; (DD) End; (S10) Calculate the priority of all pixels belonging to the boundary of an area to be reconstructed; (S20) Calculate similarity between a pixel with the highest priority and all pixels belonging to a known area; (S30) Copy patches around the pixel with the highest similarity to patches around the pixel with the highest priority; (S40) Is an area to be reconstructed nonexistent?
机译:本发明涉及一种使用结构矩阵进行基于实例的图像修复的方法,该方法能够计算属于要重构的图像数据中要重构的区域的边界的所有像素的优先级,从而确定像素。待重建区域边界中的最高优先级,计算待重建区域边界中具有最高优先级的像素与属于已知区域的所有像素之间的补丁相似度,以选择已知区域中相似度最高的像素区域,将已知区域中具有最高相似度的像素周围的补丁复制到待重建区域的边界中具有最高优先级的像素周围的补丁,以扩展已知区域并缩小要重建的区域,重复上述过程直到从要重建的图像数据中删除所有要重建的区域。因此,可以通过使用通过使用结构矩阵获得的区域的优先级来确定要重建的补丁的优先级,可以选择与要重建的补丁最相似的补丁,并且可以将要重建的图像修复为可以更自然地重建图像,从而显着提高图像修复的性能。 [参考数字](AA)开始; (BB)不; (CC)是; (DD)结束; (S10)计算属于要重构区域的边界的所有像素的优先级; (S20)计算优先级最高的像素与属于已知区域的所有像素之间的相似度; (S30)将相似度最高的像素周围的补丁复制到优先级最高的像素周围; (S40)要重建的区域不存在吗?

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