CAJ | 학술논문

针对K-SVD算法学习得到的字典结构性不强的问题,利用图像的非局部自相似性,提出了基于稀疏表示的图像分类字典学习方法( NLC-DL).该方法利用K-means对图像块进行聚类并对每个子类进行字典学习,增强字典的有效性.根据正交匹配追踪算法( OMP)求得稀疏系数,迭代优化字典,最终利用优化后字典和稀疏系数矩阵重构图像.实验结果表明:生成的学习字典对训练样本的表达误差更小,能够有效地保持图像的结构信息,重构后的图像在峰值信噪比和视觉效果方面均优于传统方法.
침대K-SVD산법학습득도적자전결구성불강적문제,이용도상적비국부자상사성,제출료기우희소표시적도상분류자전학습방법( NLC-DL).해방법이용K-means대도상괴진행취류병대매개자류진행자전학습,증강자전적유효성.근거정교필배추종산법( OMP)구득희소계수,질대우화자전,최종이용우화후자전화희소계수구진중구도상.실험결과표명:생성적학습자전대훈련양본적표체오차경소,능구유효지보지도상적결구신식,중구후적도상재봉치신조비화시각효과방면균우우전통방법.
In order to deal with the weak structure of dictionary in the K-SVD algorithm , an nonlocal classifi-cation dictionary learning method ( NLC-DL) based on sparse representation was proposed by taking advantage of image nonlocal self-similarity.The method clustered image patches with structural similarity by the K-means algorithm, then the dictionaries for each class were learned to reinforce the effectiveness.The sparse coeffi-cients obtained by the Orthogonal Matching Pursuit algorithm ( OMP) were used to optimize all the dictionaries alternately.Both the sparse coefficients and the optimized dictionaries were used for reconstructing the true im -age.Experimental results showed that the obtained dictionaries achieved a better effect with less error on re -presenting the training sample and maintained the structural information effectively.Furthermore , the proposed method for reconstructing images performed better than the traditional ones in terms of PSNR and visual effect.