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Laplacian based structure-aware error diffusion

机译:基于拉普拉斯算子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,该方案保留了图像的结构和色调相似性,同时又保持了Floyd-Steinberg误差扩散的简单性。我们的算法基于Floyd-Steinberg误差扩散算法,但修改了阈值调制部分以改善Floyd-Steinberg误差扩散算法的过模糊问题。通过将使用拉普拉斯算子获得的图像上的一些结构信息添加到量化器阈值,可以保留纹理区域中的结构细节。通过将噪声添加到阈值,可以大大减少通常在均匀区域中可见的原始错误扩散的视觉伪影。对于低对比度区域尤其如此,因为大多数现有的误差扩散算法无法保留结构细节,但我们的算法使用阈值调制将其保留得很清楚。我们的算法已使用各种类型的图像进行了评估,包括一些具有低对比度区域的图像,并使用MSSIM测量技术与其他现有的最新半色调算法进行了数字评估。结果表明,我们的方法在纹理区域和均匀区域均比现有方法具有更好的性能,并且计算速度更快。

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