[海报]基于特征约简和补偿的低维特征空间内容补全

Mariko Isogawa, Dan Mikami, Kosuke Takahashi, Akira Kojima
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引用次数: 1

摘要

提出了一种新的图像/视频内容完成框架,包括三个阶段。首先,将输入的图像/视频转换为较低维的特征空间,即使在受损区域包含复杂的结构和颜色变化的情况下,也能实现有效的恢复。其次,在转换后的特征空间中恢复受损区域;最后,从低维特征空间到原始特征空间进行逆转换,生成原始特征空间中的完整图像。这个三步解决方案有两个优点。首先,它增强了应用与原始色彩空间不同的斑块的可能性。其次,由于检索相似斑块的特征空间是唯一的扩展,因此可以使用许多现有的恢复方法,每种方法都具有不同的优点。实验验证了该框架的有效性。
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[POSTER] Content Completion in Lower Dimensional Feature Space through Feature Reduction and Compensation
A novel framework for image/video content completion comprising three stages is proposed. First, input images/videos are converted to a lower dimensional feature space, which is done to achieve effective restoration even in cases where a damaged region includes complex structures and changes in color. Second, a damaged region is restored in the converted feature space. Finally, an inverse conversion from the lower dimensional feature space to the original feature space is performed to generate the completed image in the original feature space. This three-step solution generates two advantages. First, it enhances the possibility of applying patches dissimilar to those in the original color space. Second, it enables the use of many existing restoration methods, each having various advantages, because the feature space for retrieving the similar patches is the only extension. Experiments verify the effectiveness of the proposed framework.
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