期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2015
卷号：2015
DOI：10.1155/2015/901073
出版社：Hindawi Publishing Corporation
摘要：The purpose of this paper is to design a measurement matrix of apple image based on compressed sensing to realize low cost sampling apple image. Compressed sensing based apple image sampling method makes a breakthrough to the limitation of the Nyquist sampling theorem. By investigating the matrix measurement signal, the method can project a higher dimensional signal to a low-dimensional space for data compression and reconstruct the original image using less observed values. But this method requires that the measurement matrix and sparse transformation base satisfy the conditions of RIP or incoherence. Real time acquiring and transmitting apple image has great importance for monitoring the growth of fruit trees and efficiently picking apple. This paper firstly chooses sym5 wavelet base as apple image sparse transformation base, and then it uses Gaussian random matrices, Bernoulli random matrices, Partial Orthogonal random matrices, Partial Hadamard matrices, and Toeplitz matrices to measure apple images, respectively. Using the same measure quantity, we select the matrix that has best reconstruction effect as the apple image measurement matrix. The reconstruction PSNR values and runtime were used to compare and contrast the simulation results. According to the experiment results, this paper selects Partial Orthogonal random matrices as apple image measurement matrix.
