Fast Algorithm for Nonsubsampled Contourlet Transform
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摘要: 多尺度几何分析(MGA)是一种有效的图像处理方法. 作为MGA的一种离散实现方法,非下采样轮廓波变换(NSCT)被广泛应用于图像去噪、图像融合、图像增强、特征提取等领域. 然而,由于该变换的高冗余性,其计算效率受到一定限制. 因此,对NSCT快速算法的研究具有现实意义. 本文采用一种优化的方向滤波器改进了原NSCT变换,以损失部分重建图像质量为代价,获得算法处理速度的显著提高. 实验结果可见,在满足重建图像主观质量视觉要求的前提下,算法速度可比原变换提高若干倍. 图像去噪实验进一步验证了算法的可靠性及效率.
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关键词:
- 方向滤波器 /
- 快速算法 /
- 多尺度几何分析 /
- 非下采样轮廓波变换(NSCT)
Abstract: The multiscale geometric analysis (MGA) has been recognized as an effective strategy for image processing. As one of the discrete tools of MGA, the nonsubsampled contourlet transform (NSCT) has been widely used for image denoising, image fusion, image enhancement, feature extraction and so on. However, the processing performance is limited due to its high redundancy, and leading to an intensive computational efficiency. Therefore, its fast algorithm is desired in practice. In this paper, we adopt an optimized directional filter bank (DFB) and embed it into the NSCT to significantly accelerate the computational speed while keeping slight loss of the reconstructed performance. Experimental results show that the reconstructed image quality can satisfy the human visual system. Moreover, the improved NSCT has a speed about several times than that of the traditional one. Experimental results on image denoising also validate the feasibility and efficiency of the proposed method. -
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