【摘要】 由于数字全息图的数据量庞大,其传输、存储占用大量的通信带宽和存储空间。为实现数字全息图有效的传输、存储,需要对其进行信息压缩。压缩感知理论突破了奈奎斯特采样定理的极限,能够以随机采样的方式,用远低于奈奎斯特采样定理所需要的最小数量,来精确地恢复原始信号。本文主要在同轴数字全息成像系统的基础上,引入压缩感知理论,深入研究了三种重构算法在物体全息图压缩中的影响。并分析比较出了适合数字全息图压缩重构的算法。经实验验证,适用于普通图像压缩重构的TVAL3算法,在数字全息图压缩重构中,其效果并不如Tw IST算法。更多还原

【Abstract】 Due to the huge amount of data of the digital hologram,the transmission,storage,use up a lot of communication bandwidth and storage space. To achieve efficient transmission and storage of digital holograms,information needs to be compressed. Compressed sensing theory broke the limit of Nyquist sampling theorem,it is possible to use a random sampling way to accurately recover the original signal while below the Nyquist sampling theorem of the minimum number required. In this paper,in a imaging system of in-line digital holography,introduce the features of compressive sensing theory,the thesis emphatically studied the influences of three reconstruction algorithms on digital hologram compression. We got a suitable reconstruction algorithm for digital holograms after analysis and comparison. The experiments prove that TVAL3 algorithm which is applying to ordinary image compression and reconstruction,but its effect is not well as Tw IST algorithm in digital holographic.更多还原