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Laplacian based structure-aware error diffusion

机译:基于拉普拉斯的结构感知错误扩散

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In this paper, we propose a new halftoning scheme that preserves the structure and tone similarities of images while maintaining the simplicity of Floyd-Steinberg error diffusion. Our algorithm is based on the Floyd-Steinberg error diffusion algorithm, but the threshold modulation part is modified to improve the over-blurring issue of the Floyd-Steinberg error diffusion algorithm. By adding some structural information on images obtained using the Laplacian operator to the quantizer thresholds, the structural details in the textured region can be preserved. The visual artifacts of the original error diffusion that is usually visible in the uniform region is greatly reduced by adding noise to the thresholds. This is especially true for the low contrast region because most existing error diffusion algorithms cannot preserve structural details but our algorithm preserves them clearly using threshold modulation. Our algorithm has been evaluated using various types of images including some with the low contrast region and assessed numerically using the MSSIM measure with other existing state-of-art halftoning algorithms. The results show that our method performs better than existing approaches both in the textured region and in the uniform region with the faster computation speed.
机译:在本文中,我们提出了一种新的半色调方案,可以保留图像的结构和色调相似性,同时保持弗洛伊德 - 斯坦伯格误差扩散的简单性。我们的算法基于Floyd-Steinberg误差扩散算法,但是阈值调制部分被修改为改善弗洛伊德 - Steinberg误差扩散算法的过模糊问题。通过在使用Laplacian操作者获得的图像上添加一些结构信息到量化器阈值,可以保留纹理区域中的结构细节。通过向阈值增加噪声,通常可以减少通常在均匀区域中可见的原始误差扩散的视觉伪影。对于低对比度区域尤其如此,因为大多数现有误差扩散算法不能保留结构细节,但我们的算法使用阈值调制清楚地保留它们。我们的算法已经使用各种类型的图像评估,包括一些具有低对比度区域的图像,并使用MSSIM测量与其他现有的最先进的半色调算法进行数值评估。结果表明,我们的方法比纹理区域和均匀区域中的现有方法更好地表现优于较快的计算速度。

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