第20卷
第9期
2012年9月
文章编号
1004-924X(2012)09-2095-07
光学精密工程
Optics and Precision Engineering
最小化预测残差的图像序列压缩感知
石文轩，李婕
（武汉大学电子信息学院，湖北武汉430072）
Vol. 20
No, 9 Sep.2012
摘要：提出了一种最小化预测残差的图像序列压缩感知算法以实现高速相机输出图像的实时压缩，首先，在编码端仅使用映射矩阵对原始输出图像进行压缩，将压缩得到的观测向量通过信道传输到解码端，接着，在解码端对相邻赖进行运动估计和运动补偿，得到一辐待重建图像的预测图像，利用压缩感知算法对原始图像和预测图像之间存在的预测残差图像进行重建。最后，用造代的方法优化预测残差图像的重建结果，直到连续两次的重建结果之差小于设定阔值，从而获得重建的原始图像，采用DALSA公司的CR-GEN0-H6400相机进行的实验表明，该算法可以实现1000frame/s图像的实时压缩，并且图像重建质量比独立地重建每张图像至少提高了2～6dB，有效地实现了对高速相机输出图像的实时压缩与高质量重建，
关键调：压缩感知，实时压缩；图像重建预测残差
中图分类号：TP751
文献标识码：A
doi;10.3788/OPE.20122009.2095
Imagesequencecompressedsensingbyminimizingpredictionerrors
SHIWen-xuan'LI Jie
(School of Electronic Information,Wuhan University，Wuhan 430072,China)
*Correspondingauthor，E-mail;shiwr@163.com
Abstract: An image sequence compressed sensing algorithm by minimizing prediction errors was pro-posed for high speed camera image compression in real-time. First, an original image was compressed only by a projection matrix on the encoder side, The observed vector obtained by compressing was transferred to the decoder through a channel. Then, motion estimation and motion compensation were performed on correlated images on the decoder side, and a prediction image was generated in this way. Furthermore, the prediction error image which is the difference between original image and prediction image was reconstructed by compressed sensing. Finally, the reconstruction of prediction error image was improved by an iterative procedure, until the difference between two consecutive reconstruction results was smaller than a predetermined threshold. Therefore, the original image was reconstructed by the prediction error image. Experiments by CR-GEN0-H6400 camera from DALSA indicate that the proposd algorithm can compress 1 ooo frame/s images in real-time, and image reconstruction result is im proved by 2—6 dB at least as compared with that of independent reconstruction, The proposed algorithm can compress high speed camera images in real-time, and can reconstruct the images in high quality.
Key words: compressed sensing; compress in real-time, image reconstruction; prediction error
收稿日期：2012-05-04；修订日期：2012-07-02.
基金项目：国家自然科学基金资助项目（No.61072135）