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	The Cycle Spinning-based Sharp Frequency Localized Contourlet Transform for Image Denoising
	Qu Xiaobo, Yan Jingwen
	2011-04-26
关键词	image denoising cycle spinning contourlet transform wavelet transform multiscale pyramid directional filter banks aliasing
英文摘要	Prof.YAN, as corresponding author, is advisor of Mr.Xiaobo QU.; Contourlet transform provides flexible number of directions and captures the intrinsic geometrical structure of images. The efficient directional filter banks with low redundancy of contourlet are very attractive for image processing. However, non-ideal filters are used in the original contourlet transform, especially when combined with laplacian pyramid, which results in pseudo-Gibbs phenomena around singularities for image denoising. Sharp frequency localized contourlet transform (SFLCT) is a new construction contourlet to overcome this drawback by replacing the laplacian pyramid with a new multiscale decomposition which significantly improve the denoising performance than the original form. Unfortunately, the downsampling of SFLCT makes it lack translation invariance. Thus, we employ a cycle spinning (CS) method to improve the denoising performance of SFLCT, named as cycle spinning based SFLCT (CS-SFLCT), by averaging out the translation dependence. Experimental results demonstrate that the proposed CS-SFLCT outperforms SFLCT, contourlet and cycle spinning-based contourlet for denoising in terms of PSNR and in visual effects.; This paper is supported by Navigation Science Foundation of China (No.05F07001) and National Natural Science Foundation of China (No.60472081).
语种	英语
内容类型	研究报告
源URL	[http://dspace.xmu.edu.cn/handle/2288/8421]
专题	信息技术－工作文稿
推荐引用方式 GB/T 7714	Qu Xiaobo, Yan Jingwen. The Cycle Spinning-based Sharp Frequency Localized Contourlet Transform for Image Denoising. 2011.

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