国防科技大学学报2012，Vol.34Issue(4)：126-131,6.
基于小波域字典学习方法的图像双重稀疏表示
Double sparse image representation via learning dictionaries in wavelet domain
摘要
Abstract
A novel structured dictionary training algorithm is proposed for double sparse image representation. Based on the double sparse image representation model proposed by Rubinstein, the zero-tree structure of wavelet coefficients was introduced, and the new dictionary atoms were constructed by linear combination of wavelet bases in all high-frequency bands of same orientation across different scales. The linear combination coefficients were learned via K-SVD. The image decomposition and reconstruction algorithm was proposed based on the learned dictionary. The M-term approximation and compression of remote sensing images both proved the better effects of the proposed structured dictionary than the existing dictionaries.关键词
稀疏表示/字典学习/小波/零树/图像压缩Key words
sparse representation/ dictionary learning/ wavelet/ zero-tree/ image compression分类
信息技术与安全科学引用本文复制引用
梁锐华,成礼智..基于小波域字典学习方法的图像双重稀疏表示[J].国防科技大学学报,2012,34(4):126-131,6.基金项目
国家自然科学基金资助项目(61072118) （61072118）
