摘要
A novel and efficient speckle noise reduction algorithm based on Bayesian contourlet shrinkage using contourlet transform is proposed.First,we show the sub-band decompositions of SAR images using contourle transforms,which provides sparse representation at both spatial and directional resolutions.Then,a Bayesian contourlet shrinkage factor is applied to the decomposed data to estimate the best value for noise-free contourle coefficients.Experimental results show that compared with conventional wavelet despeckling algorithm,the proposed algorithm can achieve an excellent balance between suppresses speckle effectively and preserve image details,and the significant information of origina image like textures and contour details is well ma intained.
A novel and efficient speckle noise reduction algorithm based on Bayesian contourlet shrinkage using contourlet transform is proposed.First,we show the sub-band decompositions of SAR images using contourle transforms,which provides sparse representation at both spatial and directional resolutions.Then,a Bayesian contourlet shrinkage factor is applied to the decomposed data to estimate the best value for noise-free contourle coefficients.Experimental results show that compared with conventional wavelet despeckling algorithm,the proposed algorithm can achieve an excellent balance between suppresses speckle effectively and preserve image details,and the significant information of origina image like textures and contour details is well ma intained.
基金
supported by the National Nature Science Foundation of China under Grant No. 60603083
the Nature Science Foundation of Anhu Province under Grant No. 070412059
