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	The Application of Wavelet-Based Contourlet Transform on Compressed Sensing 会议论文 OAI收割 2012 International Conference on Multimedia and Signal Processing, Shanghai, China, December 7-9, 2012 作者: Du M(杜梅); Zhao HC(赵怀慈); Zhao CY(赵春阳); Li B(李波) 收藏 \| 浏览/下载：93/0 \| 提交时间：2012/12/28 Sparse Representation Wavelet-Based Contourlet Transform Block Compressed Sensing Iterative Hard Thresholding Algorithm
	Image compression based on contourlet and no lists SPIHT (EI CONFERENCE) 会议论文 OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China 作者: Zhang S. 收藏 \| 浏览/下载：52/0 \| 提交时间：2013/03/25 The volume of raw image data captured by the high resolution camera is extremely huge. Thus the efficient image compression method should be used to decrease the bit rate. The image compression method based on wavelet is used more widely nowadays. However two dimensional wavelet is only the tensor product of the one dimensional wavelet whose support region of basis function is extended from interval to square. Contourlet is an image multiscale geometric analysis tool which could represent image sparsely and has strong capability of nonlinear approximation. The basis function of contourlet is multidirectional and anisotropic. Nevertheless contourlet is redundant. So the non-redundant Wavelet Based Contourlet Transform (WBCT) is used in this paper. The SPIHT algorithm is very efficient way to coding the significant coefficients. And the improved no lists SPIHT is more easy to implemented by hardware. Image compression method based on the combination of both wavelet based contourlet transform and no lists SPIHT coding is proposed in the paper. Experiment shows that compared to wavelet based scheme the contourlet scheme can reserve the texture of the image. For barbara test image when coding at low bit rate the PSNR can improve about 0.2dB. 2010 IEEE.
	Directional multiscale edge detection using the contourlet transform (EI CONFERENCE) 会议论文 OAI收割 2010 IEEE International Conference on Advanced Computer Control, ICACC 2010, March 27, 2010 - March 29, 2010, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States 作者: Jin L.-X.; Han S.-L.; Zhang R.-F. 收藏 \| 浏览/下载：43/0 \| 提交时间：2013/03/25 Wavelet multiresolution analysis allows us to detect edges at different scales also to obtain other important aspects of the extracted edges. However due to the usual two-dimensional tensor product wavelet transform is not optimal for representing images. The main problem in edge detection using wavelet transform is that it can only capture point-singularities and the extracted edges are not continuous. In order to solve that problem we propose a new image edge detection method based on the contourlet transform. The directional multiresolution representation Contourlet takes advantages of the intrinsic geometrical structure of images and is appropriate for the analysis of the image edges. Using the modulus maxima detection an image edge detection method based on contourlet transform is proposed. To suppress the image noise effect on edge detection the scale multiplication in contourlet domain is also proposed. Through real images experiments the proposed edge detection method's performance for the extracted edges is analyzed and compared with other two edge detection methods. The experiment result proves that the proposed edge detection method improves over wavelet-based techniques and Canny detector and also works well for noisy images. 2010 IEEE.
	Wavelet-based contourlet coding using SPECK algorithm (EI CONFERENCE) 会议论文 OAI收割 2008 9th International Conference on Signal Processing, ICSP 2008, October 26, 2008 - October 29, 2008, Beijing, China Xiu-Wei T.; Xi-Feng Z.; Tie-Fu D. 收藏 \| 浏览/下载：57/0 \| 提交时间：2013/03/25 We proposed a new still image compression algorithm called CSPECK witch was constructed by the wavelet-based contourlet transform (WBCT) and set partitioning embedded block coding (SPECK). In the WBCT the high frequency sub-band decomposed by the wavelet transform was further decomposed into multiple directional sub-bands by directional filter bank so as to explain the edge and texture of the image more sparsely. The SPECK algorithm exhibits lower complexity and more efficient compression ratio than SPIHT based on WBCT. Experimental results demonstrate that the proposed method is efficient in coding images that possess mostly textures and contours. The simulation results also show that this new coding algorithm increases the PSNR over CSPIHT. 2008 IEEE.