摘要
嫦娥探月工程二期嫦娥3号的着陆任务需要高分辨率影像数据的支持。因此,提出了一种基于压缩感知的超分辨率图像重建方法,实现了虹湾地区月球卫星影像的超分辨率重建。通过从美国阿波罗计划获取的月球影像、嫦娥1、2号卫星影像和嫦娥工程二期试验中获取的图像中提取了大量训练图块,完成了高、低分辨率过完备字典对Ah和Al的联合训练,采用正则正交基追踪算法求解优化问题,获得关于低分辨率图块的稀疏表示,并将表示系数用于Ah以生成对应的高分辨率图块,得到满足重构约束的高分辨率图像。实验验证了算法的有效性,表明其在视觉效果及PSNR和RMSE指标上均优于传统方法。
For Chang＇e-3 landing mission in the 2nd stage of Chang＇e project, high-resolution images were necessary. So a lunar satellite images super-resolution reconstruction algorithm via using compressed sensing was presented. The images from Apollo project, CE-1, CE-2 and tests in the 2nd stage of Chang＇e project were used in extracting patches and the dictionaries Ah and A1 were built with joint training. Through solving optimization problem via Regularized Orthogonal Matching Pursuit algorithm, the sparse representation for each low-resolution image patch with respect to At was obtained, and the representation coefficients were applied to Ah in order to generate the corresponding high-resolution image patch. At the end of experiment, the high-resolution image which satisfied the reconstruction constraint was achieved. Numerical experiments demonstrated the effectiveness of the proposed algorithm. Moreover, the proposed algorithm outperforms traditional methods in terms of visual quality, the Peak Signal to Noise Ratio （PSNR） and Root Mean Square Error （RMSE）.
出处
《光电工程》
CAS
CSCD
北大核心
2012年第12期86-90,共5页
Opto-Electronic Engineering
基金
国家863高技术研究发展计划资助项目(2007AA12Z318)
国家自然科学基金资助项目(41072298
40671160)
关键词
压缩感知
超分辨率
过完备字典
稀疏表达
联合训练
compressed sensing
super-resolution
over-complete dictionary
sparse representation
joint training
