一种基于小波稀疏确定性简单二值观测矩阵的嵌入式视觉压缩传感实现方法  

Novel Deterministic Simple 0-1 Observation Matrix and Wavelet Sparsity Based Compressed Sensing Implementation Method for Embedded Vision System

作　　者：刘继忠[1] 金明亮[1] 马如远[2] 柴国钟[2] 

机构地区：[1]南昌大学机器人研究所,江西南昌330031 [2]浙江工业大学机械学院,浙江杭州310000

出　　处：《中国激光》2013年第11期223-228,共6页Chinese Journal of Lasers

基　　金：国家自然科学基金(61273282);江西省教育厅自然科学基金(GJJ12005)

摘　　要：由于传统的高斯矩阵、伯努利矩阵等观测矩阵在硬件上实现比较困难,观测矩阵的构建一直是压缩传感硬件实现的关键问题之一,因此,选择构建合适的观测矩阵,对于嵌入式视觉压缩传感的实现具有重要的意义。针对嵌入式视觉压缩传感实现的可行性与实时性,通过稀疏变换矢量化和伪随机序列观测矩阵构建相关研究,结合小波稀疏起主要作用系数数据位于矢量矩阵前侧,提出了一种基于小波稀疏的确定性简单二值观测矩阵的压缩传感实现方法。对于N×N图像,在观测值M下,观测矩阵由M个不相同的N维基向量组成,每个向量只有一个元素1,其余元素为0,结构简单,在实际观测测量中省去了积分电路,提高了效率,而且每个基向量可以根据元素1的位置,按照1-M确定性排列,构成确定性观测矩阵,便于观测矩阵存储和压缩传感恢复重构。The construction of an appropriate observation matrix is the key issue for compressed sensing practical application in embedded system. However, the matrix usually used, like Gaussian matrix, Bernoulli matrix etc, is difficult to realize for hardware. Aiming at the feasibility and the real-time of compressive sensing for embedded vision system, a simple deterministic 0-1 observation matrix is proposed, which is based on the related work of sparse transform vectorization, the characteristic of wavelet sparsity, and the pseudo-random sequence observation matrix construction. For N× N image, when the observation matrix dimension is M × N, the matrix is consist of M base vectors and the vector size is N. The base vector has only one element of 1 and the other elements are 0. So the formed observation matrix is simple and the integral circuit can be omitted in the real measurement. Base vectors are arranged according to the position of element 1 in vector from 1 to M to form a deterministic observation matrix, which is easy to remember and store the matrix, and also helpful for reconstruction.

关 键 词：图像处理 嵌入式视觉系统 压缩传感 观测矩阵 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置] TP242.6[自动化与计算机技术—控制科学与工程]