激光与光电子学进展, 2020, 57 (8): 081018, 网络出版: 2020-04-03  

图像边缘信息辅助的压缩采样策略 下载: 889次

Image Edge Information Aided Compressive Sampling Strategy
作者单位
1 嘉兴学院数理与信息工程学院, 浙江 嘉兴 314001
2 嘉兴国电通新能源科技有限公司, 浙江 嘉兴 314001
3 黑龙江大学数据科学与技术学院, 黑龙江 哈尔滨 150080
摘要
压缩感知是近年来提出的一种新的信号压缩采样理论,采样端通过投影获取压缩数据,需要较多计算资源并且成本较高,仍未能实现广泛应用。不同于标准的压缩感知,本文提出一种基于边缘信息辅助的图像压缩采样方法,即随机采集图像的部分像素作为测量,其中图像边缘附近像素以较高的概率采样,最后使用非线性优化方法恢复图像。所提采样策略通过两次采样分别获取随机测量值及自适应测量值,并给出采样策略的物理描述,以及仿真实验实现,同时讨论了边缘信息在采样矩阵中的最优比率。实验结果表明,所提算法能快速有效地恢复高质量图像。
Abstract
Compressive sensing (CS) is proposed as a new signal compressive sampling theory in recent years. At the coding end CS obtains compressed data through projection, which requires more computing resources and higher implementation cost. Different from the standard compressed sensing, this paper proposes an image compression sampling method based on edge information assistance. In other words, some pixels of the image are randomly collected as measurement, and the pixels near the image edge are sampled with a high probability. Finally, the nonlinear optimization method is used to restore the image. The proposed sampling strategy obtains the random measurements and the adaptive measurements respectively through two steps. This paper gives the physical description of the sampling strategy and realizes it through simulation experiment. At the same time, the optimal ratio of edge information in sampling matrix is also discussed. Experimental results show that the proposed algorithm can quickly and effectively recover high quality images.

杨俊, 潘博, 陈丽, 朱永安, 蒋涛, 崔晨. 图像边缘信息辅助的压缩采样策略[J]. 激光与光电子学进展, 2020, 57(8): 081018. Jun Yang, Bo Pan, Li Chen, Yongan Zhu, Tao Jiang, Chen Cui. Image Edge Information Aided Compressive Sampling Strategy[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081018.

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