【摘要】：近年來隨著多尺度分析和壓縮感知成為研究的熱點,字典學習算法在圖像融合領域得到了廣泛應用,但是其算法應用于可見光和紅外圖像的融合,容易出現(xiàn)塊狀噪聲,邊緣有振鈴現(xiàn)象。基于此,本文提出了一種基于非下采樣剪切波變換(NSST)和字典學習的紅外和見光圖像融合算法研究,對NSST分解的低頻分量利用滑動窗口得到圖像塊序列,并對其進行零均值化后再稀疏分解,選擇區(qū)域能量的融合規(guī)則,高頻子帶選擇拉普拉斯能量和的融合規(guī)則。仿真結果表明,本文的算法在視覺和客觀評價指標上優(yōu)于現(xiàn)有幾種融合算法。
[Abstract]:In recent years, with the research of multi-scale analysis and compression perception, dictionary learning algorithm has been widely used in the field of image fusion, but its algorithm is applied to the fusion of visible and infrared images, which is prone to appear block noise. There is a ringing on the edge. Based on this, this paper proposes an infrared and visible image fusion algorithm based on non-downsampling shear wave transform (NSST) and dictionary learning. The low frequency components of NSST decomposition are obtained by sliding window. After zero mean decomposition, the fusion rules of region energy and Laplacian energy sum are selected in the high frequency subband and the fusion rule of Laplace energy sum is chosen. The simulation results show that the proposed algorithm is superior to the existing fusion algorithms in visual and objective evaluation.
【作者單位】： 西北工業(yè)大學電子信息學院;西北工業(yè)大學保密處;西北工業(yè)大學計算機學院;
【基金】：國家自然科學基金(61071171)資助
【分類號】：TP391.41

【參考文獻】

相關期刊論文 前5條

1 楊森林;萬國賓;;基于分塊壓縮感知的遙感圖像多尺度融合[J];西北大學學報(自然科學版);2015年03期

2 李暉暉;曾艷;楊寧;姚西文;錢林弘;;改進的壓縮感知重構算法及其在圖像融合中的應用[J];南京理工大學學報;2014年02期

3 王s，

本文編號：2409090