A DENOISING ALGORITHM RAISED ON NONSUBSAMBLED CONTOURLET TRANSFORM AND TWO-DIMENSIONAL PRINCIPL COMPONENT ANALYSIS
This paper proposes a novel image denoising algorithm which utilizes noise energy to perform image denoising based on Two-Dimensional Principal Component Analysis (TDPCA) in Nonsubsampled Contourlet Transform (NSCT) domain.The NSCT can capture the edges of natural images efficiently, and at the same time, it can get rid of the Gibbs effect. The noisy image can be decomposed by the NSCT into directional subbands.The TDPCA is then used to estimate the noise energy (local threshold) for the image blocks in high frequency subbands.The soft threshold shrinkage can hence be employed on the NSCT coefficients without estimating the noise variance. At last, the inverse NSCT is carried out on the modified coefficients to obtain the denoised image. The denoising algorithm is validated by numerical experiments on two typical images.Numerical results show that the proposed method is superior both in vision and in PSNR to former methods.
Image denoising Contourlet Transform Nonsubsampled Contourlet Transform Principal Component Analysis Two-Dimension Principal Component Analysis Soft Threshold
HE KUN-XIAN XIAO YAN-CHANG WANG XIAO-BING WANG QING HE FAN
School of Instrument Science and Engineering,Southeast University School of Instrument Science and Engineering,Southeast University China Jiliang University Nanjing Artillery Academy Communication &Power Utilization Subcompany,China Electric Power Research Institute
国际会议
成都
英文
1879-1885
2011-11-25（万方平台首次上网日期，不代表论文的发表时间）