针对煤矿井下无线传感网络因信息传输量大而导致传感节点能量消耗快、设备寿命缩减的问题,在分析矿山物联网的特点和小波变换系数层的基础上,提出一种基于小波域分块的多层小波变换自适应压缩感知图像处理算法.该方法首先采用bior4.4小波变换对煤矿井下图像信号进行多层分解,针对小波域中不同的区域采用不同的测量矩阵对稀疏化的图像数据进行测量,得到测量数据,然后利用稀疏自适应匹配追踪算法(SAMP)恢复高频系数,最后将高频系数和低频系数一起进行小波逆变换重构图像.仿真实验表明:与传统的压缩感知算法相比,该算法不仅能够以更低的采样率获得高质量的重构图像效果,压缩了图像大小,且能够快速地还原图像.%Aiming at the problem that the energy consumption of the sensor node is fast and the equipment life is reduced due to the large amount of information transmission in coal mine wireless sensor networks , it analyzes the characteristics of mine object network and wavelet transform coefficient layer , proposes an adaptive image pro-cessing algorithm for multi -layer wavelet transform based on wavelet domain segmentation .It uses bior4.4 wavelet transform to decompose the down -hole image signal of coal mine , uses different measurement matrices to measure the sparse image data and get the measurement data in the different regions .The sparse adaptive matching pursuit algorithm ( SAMP) is used to recover the high frequency coefficients , and finally the high fre-quency coefficients and the low -frequency coefficients are transformed together to reconstruct the image .Simu-lation results show that compared with the traditional compressed sensing algorithm , the algorithm can obtain high quality recon-build image at lower sampling rate , compress the image size and quickly restore the image .
展开▼
