The upconversion luminescences of YAlO3:Er3+ phosphor co-doping with different Gd3+ concentrations are investigated under the excitation of 980- and 532-nm diode lasers. A near ultraviolet upconversion emission at 410 nm is observed in YAlO3 under 532-nm excitation. Moreover, the inactive Gd3+ ions can improve the upconversion intensity efficiently in a certain range of concentration. Under 980-nm excitation, the visible upconversion emissions at 546 and 646 nm are enhanced by about 10 and 8 times at the Gd3+ concentration of 40%, respectively. The upconversion emission at 410 nm under 532-nm excitation is also enhanced by 7 times. The substitution of Gd3+ ions for Y3+ sites changes the local symmetry of Er3+, leading to the improvement of upconversion efficiency.

针对有些图像增强算法对噪声比较敏感,提出了一种自适应图像增强算法。利用原算法的基本原理,结合人眼的视觉特性自适应地生成算法的参数,这样首先保留了原算法实现容易、运算速度快等特点,其次,对噪声具有一定的抑制作用,再次,可以增强和保留图像细节信息。通过实验比较,所提算法能够自适应地生成算法的参数,并在保持了原算法原有的容易实现的优点的基础上,提高了算法抑制噪声方面的性能,将该算法应用于彩色图像增强也取得了相当理想的结果。

提出了一种基于多阈值分割和无下采样Contourlet变换(Nonsubsampled Contourlet Transform, NSCT)的多光谱与高分辨率图像融合算法。对多光谱图像进行多阈值分割, 并利用提出的区域均值比指标将多光谱图像划分为需要进行空间细节增强及需要保持光谱特征的区域; 然后利用NSCT对高分辨率图像和多光谱图像的强度分量进行多尺度、多方向分解。分解后的低频部分采用基于窗口邻域的融合规则和算子进行融合, 高频部分按区域均值比指标进行区域融合; 最后进行重构得到融合后的多光谱图像的强度分量, 经IHS逆变换后得到高分辨率的多光谱图像。实验结果表明, 该算法可获得较理想的融合图像, 融合效果优于IHS变换法、基于像素的à trous小波变换法以及基于像素的NSCT法。

提出了一种基于区域分割和Contourlet变换的图像融合算法。首先,对各源图像做区域分割,并利用区域能量比和区域清晰比的概念来度量和提取区域信息;然后,对各源图像进行多尺度非子采样Contourlet分解,分解后的高频部分采用绝对值取大算子进行融合,低频部分则采用基于区域的融合规则和算子进行融合;最后进行重构得到融合图像。对红外与可见光图像进行了融合实验,并与基于像素的à trous小波变换和Contourlet变换的融合效果进行了比较。结果表明,采用本文算法的融合图像既保留了可见光图像的光谱信息,又继承了红外图像的目标信息,其熵值高于基于像素的融合方法约10％,交叉熵仅为基于像素的融合方法的1％左右。

A novel image fusion algorithm based on wavelet-based contourlet transform (WBCT) and principal component analysis (PCA) is proposed. The PCA method is adopted for the low-frequency components. Using the proposed algorithm to choose the greater of the active measures, the region consistency test is performed for the high-frequency components. Experiments show that the proposed method works better in preserving the edge and texture information than wavelet transform method and Laplacian pyramid (LP) method do in image fusion. Four indicators for the fusion image are given to compare the proposed method with other methods.