CAJ | 학술논문

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.
침대양본도상자전자괄응성차、유효신식단일、조성도상희소표시모호적불족적문제, 제출일충기우특정분류학습자전적결구희소전파도상수복방법. 수선장도상괴안특정분류, 근거불동특정적도상양본진행양본훈련득도상대응적과완비자전; 연후대불동특정적대수복도상괴제취불동적유효신식진행희소편마, 사득희소표시구유교강적자괄응능력; 최후침대결구희소전파모형대래적편차진행수개, 완선결구희소적전파궤제. 방진실험결과표명,해방법가이유효지수복도상결구변연、불규칙문리화평활부분적도상신식, 수복후적도상질량유교대적제승.
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.