An Improved Sparse Representation De-noising for Keeping Structural Features

摘要：

　　Considering the current image de-noising methods may lose some structural features,this paper proposes an improved sparse representation based method by adopting the histogram structural similarity.When the initial over-complete dictionary was applied in the sparse decomposition,similarity factor could replace the reconstruction error as the factor of fidelity.The orthogonal matching pursuit algorithm(OMP) is used to reconstruct the denoised image.The experimental results show that the proposed method could provide better PSNR and HSSIM results compared with the wavelet transformation,the K-SVD algorithm and the method presented in 10,meanwhile,and the structural features can be reserved effectively by the proposed method.

关键词： Structural feature Similarity factor Sparse representation Image de-noising

作者: Zhi Cui

作者单位: School of Communication and Electronic Engineering,Hunan City University,Hunan Yiyang 413000,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 253-262

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）