Directional multiscale modeling of images using the contourlet transform
Directional multiscale modeling of images using the contourlet transform
复制标题
DOI：
10.1109/ssp.2003.1289394
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Duncan D. Po;Minh N. Do
中科院分区：
文献类型：
--
作者：
Duncan D. Po;Minh N. Do
轮廓波变换是一种新的扩展，小波变换在二维使用不可分离和方向滤波器组。由于它的多尺度和方向性，它可以有效地捕捉图像的边缘沿着一维轮廓与少量的系数。本文研究了轮廓波变换域的图像建模及其应用。我们开始与contourlet系数的统计，这揭示了他们的非高斯边缘统计和强依赖性的详细研究。条件相邻系数的幅度，轮廓波系数被发现是近似高斯。基于这些统计数据，我们构建了一个轮廓波隐马尔可夫树（HMT）模型，可以捕获所有的轮廓波的尺度间，方向间和子带内的依赖性。我们实验使用该模型在图像去噪和纹理检索。在去噪方面，Contourlet HMT在视觉质量和峰值信噪比（PSNR）方面优于小波HMT和其他经典方法。在纹理检索中，它表现出各种方向的纹理小波方法的性能改善。
The contourlet transform is a new extension to the wavelet transform in two dimensions using nonseparable and directional filter banks. Because of its multiscale and directional properties, it can effectively capture the image edges along one-dimensional contours with few coefficients. This paper investigates image modeling in the contourlet transform domain and its applications. We begin with a detail study of the statistics of the contourlet coefficients, which reveals their non-Gaussian marginal statistics and strong dependencies. Conditioned on neighboring coefficient magnitudes, contourlet coefficients are found to be approximately Gaussian. Based on these statistics, we constructed a contourlet hidden Markov tree (HMT) model that can capture all of contourlets' inter-scale, inter-orientation, and intra-subband dependencies. We experiment using this model in image denoising and texture retrieval. In denoising, contourlet HMT outperforms wavelet HMT and other classical methods in terms of both visual quality and peak signal-to-noise ratio (PSNR). In texture retrieval, it shows improvements in performance over wavelet methods for various oriented textures.