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Image dequantization: Restoration of quantized Colors

机译:图像反量化:还原量化的色彩

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摘要

Color quantization replaces the color of each pixel with the closest representative color, and thus it makes the resulting image partitioned into uniformly-colored regions. As a consequence, continuous, detailed variations of color over the corresponding regions in the original image are lost through color quantization. In this paper, we present a novel blind scheme for restoring such variations from a color-quantized input image without a priori knowledge of the quantization method. Our scheme identifies which pairs of uniformily-colored regions in the input image should have continuous variations of color in the resulting image. Then, such regions are seamlessly stitched through optimization while preserving the closest representative colors. The user can optionally indicate which regions should be separated or stitched by scribbling constraint brushes across the regions. We demonstrate the effectiveness of our approach through diverse examples, such as photographs, cartoons, and artistic illustrations.
机译:颜色量化将每个像素的颜色替换为最接近的代表颜色,从而使生成的图像划分为均匀着色的区域。结果,通过颜色量化丢失了原始图像中相应区域上颜色的连续,详细的变化。在本文中,我们提出了一种新颖的盲目方案,可在不事先了解量化方法的情况下从颜色量化的输入图像中还原此类变化。我们的方案确定输入图像中的哪对均匀色区域应该在结果图像中具有连续的颜色变化。然后,在保留最接近的代表色的同时,通过优化无缝拼接这些区域。用户可以通过在整个区域上进行划线约束笔刷来选择指示哪些区域应该分开或缝合。我们通过各种示例(例如照片,卡通和艺术插图)来证明我们的方法的有效性。

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