基于剪切波变换的改进全变分散斑去噪方法

Shearlet-Transform-Based Improved Total Variation Speckle Denoising Method

在散斑去噪过程中保持图像边缘纹理特征,是光学相干层析图像处理技术的难题。散斑去噪过程中的散斑残留和边缘纹理模糊是该难题的主要诱导因素。为解决这一难题,提出一种基于剪切波变换的改进全变分散斑去噪方法。该方法结合剪切波变换和传统全变分模型,对不同图像区域采用针对性的去噪策略,兼顾散斑去噪与纹理保留,提高了光学相干层析图像的噪声抑制效果。对不同生理、病理状态下的视网膜光学相干层析图像进行测试,结果表明:该方法通过采用区域针对性策略改进了噪声抑制能力,通过引入剪切波变换方法提高了边缘纹理保持能力,进而同时实现散斑去除和纹理保留。此外,与其他散斑去噪方法进行对比,验证了该方法的有效性。

In the field of optical coherence tomography,reducing the speckle noise while protecting the textural features of image edge is difficult mainly because of the speckle residue and textural blur of edge in the speckle denoising process.To solve this problem,this study proposes a shearlet-transform-based improved total variation speckle denoising method.By combining the shearlet transform with the traditional total variation model,as well as a targeted denoising strategy applied on different image regions,the proposed method reduces the speckle noise without disturbing the texture in the image,and further improves the speckle-noise suppression in the original optical coherence tomography image.The proposed method is tested on many retinal optical coherence tomography images under different physiological and pathological conditions.Results show that the regional targeted strategy in the proposed method improves the ability of speckle-noise suppression,while the shearlet transform improves the ability of the edge texture protection,resulting in simultaneous speckle reduction and texture protection.The effectiveness of the proposed method is also confirmed in comparison with other common speckle denoising methods.