Image Denoising Algorithm Based on the Second Generation Curvelet Transform
Curvelet transform is a new extension to wavelet transform in two dimensions. Curvelet overcomes the limitation of wavelet in analyzing signals with dimension higher than 1-D because it has the character of anisotropy. The second generation curvelet transform theory makes it understood and implemented more easily. A novel image denoising method based on the second generation curvelet transform is proposed. The nonlinear hyperbolic tangential function of neural network is selected as curvelet thresholding function in this paper. Firstly, the noisy image is decomposed by the curvelet transform, then the curvelet coefficients are processed with thresholding function, and finally the processed coefficients are reconstructed by the inverse curvelet transform to obtain denoised result. Experiments show that our method yields denoised images with higher PSNR value and better visual quality.
Qingwu Li Yingzi Song Xue Ni Dan Shi
College of Computer & Information Engineering Hohai University Changzhou, 213022
国际会议
南宁
英文
2007-07-20（万方平台首次上网日期，不代表论文的发表时间）