江西理工大学学报
江西理工大學學報
강서리공대학학보
JOURNAL OF JIANGXI UNIVERSITY OF SCIENCE AND TECHNOLOGY
2014年
5期
73-78
,共6页
压缩传感%凸优化算法%贪婪迭代算法%最速下降法%正交匹配算法
壓縮傳感%凸優化算法%貪婪迭代算法%最速下降法%正交匹配算法
압축전감%철우화산법%탐람질대산법%최속하강법%정교필배산법
compressed sensing%convex optimization algorithm%greedy iterative algorithm%the steepest descent method%orthogonal matching algorithm
压缩传感应用于图像压缩重构的算法通常有凸优化算法和贪婪迭代算法两大类。一般而言,凸优化算法重构概率高、速度较慢,贪婪迭代算法具有较快的重构速度,但损失了重构质量。结合凸优化算法中的最速下降法及贪婪迭代算法中的正交匹配算法(OMP),提出了一种新的算法,并应用于一维信号和二维图像信号的压缩重构实验,且深入对比分析了不同降采样矩阵对新算法的影响。结果发现,对同一降采样矩阵,即使图像的纹理不同,新算法在重构质量及重构时间上都优于原始的OMP算法。
壓縮傳感應用于圖像壓縮重構的算法通常有凸優化算法和貪婪迭代算法兩大類。一般而言,凸優化算法重構概率高、速度較慢,貪婪迭代算法具有較快的重構速度,但損失瞭重構質量。結閤凸優化算法中的最速下降法及貪婪迭代算法中的正交匹配算法(OMP),提齣瞭一種新的算法,併應用于一維信號和二維圖像信號的壓縮重構實驗,且深入對比分析瞭不同降採樣矩陣對新算法的影響。結果髮現,對同一降採樣矩陣,即使圖像的紋理不同,新算法在重構質量及重構時間上都優于原始的OMP算法。
압축전감응용우도상압축중구적산법통상유철우화산법화탐람질대산법량대류。일반이언,철우화산법중구개솔고、속도교만,탐람질대산법구유교쾌적중구속도,단손실료중구질량。결합철우화산법중적최속하강법급탐람질대산법중적정교필배산법(OMP),제출료일충신적산법,병응용우일유신호화이유도상신호적압축중구실험,차심입대비분석료불동강채양구진대신산법적영향。결과발현,대동일강채양구진,즉사도상적문리불동,신산법재중구질량급중구시간상도우우원시적OMP산법。
When Compressed Sensing (CS) model is applied to the compression-reconstruction of the image, the solving algorithm usually can be categorized as convex optimization algorithm and greedy iterative algorithm. In general, the convex optimization algorithm has a higher reconstruction rate but a slower speed, while the greedy iterative algorithm has a faster speed with a loss of the reconstruction quality. Based on one of convex optimization algorithm, the Orthogonal Matching Algorithm (OMP), and one of greedy iterative algorithm, the steepest descent method, a new algorithm is proposed in this paper. And the new algorithm is applied to one dimensional signal and two-dimensional image signal for compression - reconstruction experiments. The different downsampling matrix effect of this algorithm is also analyzed. Results show that for the same downsampling matrix, the new algorithm is better than the original OMP algorithm both in terms of the reconstruction quality and the reconstruction time when applied for images with different textures.