关键词: 整体变分, 小波阈值去噪, 自适应正则化, 各向异性扩散, 滞后扩散定点迭代

Abstract: A new adaptive total variation filtering method is presented, which depends on local information of signal. Wavelet threshold filtering is introduced. Regularization term in total variation is replaced by adaptive regularization term. Filtering strength is adjusted adaptively between total variation regularization and isotropic smoothing. Lagged diffusivity fixed point iteration is used to solve total variation minimization problem. The numerical experiments show that staircasing effect is reduced. The pseudo-Gibbs phenomenon is restrained. The detected edge and locations of discontinuity point, of the reconstructed signal, are very exact. And, the SNR is distinctly improved.

Key words: total variation, wavelet threshold denoising, adaptive regularization, anisotropic diffusion, lagged diffusivity fixed point iteration