Image NSST-HMT model with associated multi-state coefficients
Xianghai Wang, Chuanming SONG, Yihuan Zhu, Xiaoyang ZHAO, Ruoxi Song · Scientia Sinica Informationis · 2018
In recent years, due to its anisotropy, multi-directional capture characteristics,and translation invariance, the non-subsampled shearlet transform (NSST) hasplayed an important stabilizing role inthe process of image restoration. In this study, weanalyze an image's NSST coefficients, including the relationship betweencoefficients in the same subband, the relationship between“father-son” coefficients, and the relationship between “brotherhood”coefficients in different subbands. The results reveal that thecoefficients in the NSST subbands are sparse, and both “father-sonrelationship” and “brotherhood relationship” coefficients exhibitaggregation and transitivity. On this basis, a hidden Markov tree (HMT)model with associated multi-state coefficients (M-NSST-HMT) isproposed. This model estimates the reconstructed coefficientsusing “father-son relationship” and “brotherhood relationship”of the NSST subband coefficients as joint states of guiding thecoefficients' transfer between subbands. In addition, the model integrates thereconstructed coefficients using the mutual information betweenthese two associated states. Finally, the proposed model is appliedto image denoising with favorable results. The results indicatethat the proposed model can reveal the relationship ofcoefficients in NSST subbands and improve the prediction accuracy ofcoefficients more effectively than the traditional HMT model.