摘要
针对样本图像字典自适应性差、有效信息单一、造成图像稀疏表示模糊的不足的问题,提出一种基于特征分类学习字典的结构稀疏传播图像修复方法.首先将图像块按特征分类,根据不同特征的图像样本进行样本训练得到相对应的过完备字典;然后对不同特征的待修复图像块提取不同的有效信息进行稀疏编码,使得稀疏表示具有较强的自适应能力;最后针对结构稀疏传播模型带来的偏差进行修改,完善结构稀疏的传播机制.仿真实验结果表明,该方法可以有效地修复图像结构边缘、不规则纹理和平滑部分的图像信息,修复后的图像质量有较大的提升.
Sample image dictionary has poor adaptability and simplex valid information, which results in bad image sparse representation. Because of the shortage, this paper discusses a new image inpainting method by characteristics classification learning and patch sparsity propagation. The proposed method classified the image patches by their different characteristics firstly, then got the corresponding over-complete dictionary by training the image blocks of different characteristics and extracted different valid information from these blocks for sparse coding, which makes the sparse representation to have stronger adaptive capacity. Finally, the propagation mechanisms can be improved by modifying the patch sparsity propagation model.Experi-ment results show that the proposed method can work on the edge, irregular textures and smooth portion ef-fectively and make the image quality higher.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2015年第5期864-872,共9页
Journal of Computer-Aided Design & Computer Graphics
关键词
特征分类
局部方差
分类稀疏表示
结构稀疏
Mean-Shift
K-SVD
characteristic classification
local variance
Mean-Shift
K-SVD
classified sparse representation
patch sparsity propagation
