吕海波1,
李秋富1,
宫久路1,,,
厉智强2,
韩肖君3
1.北京理工大学? ?北京? ?100081
2.北京宇航系统工程研究所? ?北京? ?100076
3.北京航宇天穹科技有限公司? ?北京? ?100043
详细信息
作者简介:谌德荣：女，1966年生，博士，教授，研究方向为信息处理、自动目标识别等
吕海波：男，1993年生，硕士生，研究方向为图像压缩处理等
宫久路：男，1983年生，博士，讲师，研究方向为数字信号处理、模式识别等
通讯作者:宫久路 lujiugong@bit.edu.cn
中图分类号:TN919.81计量
文章访问数:1726
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被引次数:0
出版历程
收稿日期:2018-09-30
修回日期:2019-02-18
网络出版日期:2019-03-23
刊出日期:2019-09-10
Total Variation Regularized Reconstruction Algorithms for Block Compressive Sensing
Derong CHEN1,Haibo Lü1,
Qiufu LI1,
Jiulu GONG1,,,
Zhiqiang LI2,
Xiaojun HAN3
1. Beijing Institute of Technology, Beijing 100081, China
2. Beijing Institute of Astronautical Systems Engineering, Beijing 100076, China
3. Beijing HYTQ Technology Ltd. Co., Beijing 100043, China
摘要
摘要:针对分块压缩感知(BCS)重建图像质量较差问题，该文提出一种最小化l0范数的分块压缩感知全变差(TV)正则化迭代阈值图像重构算法(BCS-TVIT)。BCS-TVIT算法考虑图像的局部平滑、有界变差等性质，将最小化l0范数与图像的全变差TV正则项结合，构建目标函数。针对目标函数中l0范数项和分块测量约束项无法直接优化问题，采用迭代阈值法使重构图像l0范数最小化，并通过凸集投影保证满足约束条件，完成了目标函数的优化求解。实验表明，与基于l0范数最小化的分块压缩感知平滑投影算法(BCS-SPL)相比，BCS-TVIT算法重构图像峰值信噪比提高2 dB，能消除BCS-SPL的“亮斑”效应，且在视觉效果上明显优于BCS-SPL算法；与最小全变差算法相比，BCS-TVIT算法重构图像峰值信噪比提升1 dB，且能降低重构时间约2个数量级。
关键词:分块压缩感知/
l0范数/
全变差/
阈值滤波/
凸集投影
Abstract:In order to improve the quality of reconstruction image by Block Compressed Sensing (BCS), a Total Variation Iterative Threshold regularization image reconstruction algorithm (BCS-TVIT) is proposed. Combining the properties of local smoothing and bounded variation of the image, BCS-TVIT uses the minimization l0 norm and total variation to construct the objective function. To solve the problem that l0 norm term and the block measurement constraint can not be optimized directly, the iterative threshold method is used to minimize the l0 norm of the reconstructed image, and the convex set projection is employed to guarantee the block measurement constraint condition. Experiments show that BCS-TVIT has better performance than BCS-SPL in PSNR by 2 dB. Meanwhile, BCS-TVIT can eliminate the " bright spot” effect of BCS-SPL, having better visual effect. Comparing with the minimum total variation, the proposed algorithm increases PSNR by 1 dB, and the reconstruction time is reduced by two orders of magnitude.
Key words:Block Compressed Sensing (BCS)/
l0 norm/
Total Variation (TV)/
Threshold filtering/
Convex set projection
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https://jeit.ac.cn/article/exportPdf?id=c9bf7315-afec-4a96-914f-13786bd5fab1
