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Two-stage false contour detection algorithm using re-quantization and directional contrast features and its application to adaptive false contour reduction

机译:两级假轮廓检测算法使用重新量化和定向对比度特征及其在自适应假轮廓减少的应用中的应用

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This paper proposes a two-stage false contour detection algorithm using re-quantization and directional contrast features, with application to false contour reduction. In the first stage of false contour detection, smooth regions are first removed by bit-depth reduction and re-quantization. In the second stage, false contours are separated from edge or texture regions using directional contrast features, yielding the false contour detection map that shows possible candidate regions of false contours. For false contour reduction, adaptive directional smoothing filtering is applied only to candidate regions that are specified by the false contour detection map. Therefore the proposed algorithm is able to preserve sharp edges and details. Computer simulation with a large number of test images shows the effectiveness of the proposed algorithm.
机译:本文提出了一种使用重新量化和方向对比度特征的两级假轮廓检测算法,应用于假轮廓减少。在假轮廓检测的第一阶段,首先通过比特深度减少和重新量化来删除平滑区域。在第二阶段,使用方向对比度分开假轮廓与边缘或纹理区域分离,产生假的轮廓检测图,其显示出假轮廓的可能候选区域。对于假轮廓降低,自适应定向平滑滤波仅应用于由假轮廓检测图指定的候选区域。因此,所提出的算法能够保留尖锐的边缘和细节。具有大量测试图像的计算机模拟显示了所提出的算法的有效性。

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