西安电子科技大学学报（自然科学版）Issue(1)：12-17,109,7.DOI:10.3969/j.issn.1001-2400.2016.01.003
结合自适应稀疏表示和全变分约束的图像重建
Adaptive sparse representation and total variation constraint based image reconstruction
摘要
Abstract
In view of the limitation of fixed complete orthogonal transformation , represented by two -dimensional wavelet transform and discrete cosine transform in compressed sensing high-resolution image reconstruction , this paper proposes a new method for high-resolution image reconstruction based on adaptive redundant dictionary sparse representation with the total variation constraint .The algorithm takes the intermediate image in the process of iteration as the training sample to get a redundant dictionary suitable for sample characteristics by adaptive learning . It makes full use of the correlation between dictionary atoms and the image to get an ideal complete sparse representation , thus reducing the sampling rate and improving the quality of image reconstruction . Finally , the algorithm takes the total variation as a constraint and uses the split Bregman iterative method to solve the sparse optimization problem . Simulation shows that the proposed method can reconstruct high quality images under a low sampling rate .关键词
压缩感知/自适应冗余字典/稀疏表示/图像重建/全变分Key words
compressed sensing/adaptive redundant dictionary/sparse representation/image reconstruction/total variation分类
信息技术与安全科学引用本文复制引用
王勇,冯唐智,陈楚楚,乔倩倩,杨笑宇,王国栋,高全学..结合自适应稀疏表示和全变分约束的图像重建[J].西安电子科技大学学报（自然科学版）,2016,(1):12-17,109,7.基金项目
国家自然科学基金资助项目（61271296）；中央高校基本科研业务费专项资金资助项目（JB150218）；西安电子科技大学教育教学改革研究资助项目（B1311）；西安电子科技大学新实验开发与新实验设备研制及实验教学改革资助项目 （）
