光电工程, 2010, 37 (6): 73, 网络出版: 2010-09-07
架构于抗混叠轮廓波的红外图像插值放大算法
An Infrared Image Interpolation Algorithm Using Aliasing-free Contourlet
抗混叠轮廓波变换 滤波器组 红外图像 图像插值 aliasing-free contourlet transform filter banks infrared image image interpolation
摘要
为了在放大红外图像时保持边缘或轮廓的正则性,提出一种基于抗混叠轮廓波变换的图像插值放大算法。该算法首先结合抗混叠塔式滤波器组和方向滤波器组,构造出抗混叠的轮廓波变换;然后将原始图像的小波域线性插值结果看成是放大图像的初始估计,输入到一个迭代过程;在每次迭代中,将放大图像看成是理想高分辨率图像的含噪逼近,并对其实施抗混叠轮廓波变换,根据变换系数的稀疏性约束实现降噪处理;最后,经过若干次迭代得到理想的红外放大图像。实验表明,对于测试图像,经过迭代处理后峰值信噪比平均提高了0.837 dB;且该算法在视觉质量上明显优于双线性插值算法及基于小波的算法。
Abstract
In order to keep the regularity of edges or contour in the magnification of infrared image, an infrared image interpolation algorithm using Aliasing-free Contourlet Transform (AFCT) is proposed. Firstly, Aliasing-free Pyramidal Filter Banks (AFPFB) is combined with Directional Filer Banks (DFB) to construct AFCT. Then, a simple wavelet-based linear interpolation result is used as the initial estimate of the magnified image and fed in an iterative process. In the each iteration, the estimation of the interpolated image is viewed as a noisy version of the ideal high-resolution image and imposing AFCT to it. Subsequently, the denoising process is implemented via the sparsity constraint of the AFCT coefficients. Finally, the magnified infrared image is obtained after several iterations. Experiments show that for the test images, the iterative processing improves the Peak Signal to Noise Ratio (PSNR) with an average 0.837 dB. Moreover, the proposed algorithm obviously outperforms traditional methods based on bilinear interpolation or wavelet in terms of visual quality.
金炜, 尹曹谦, 周亚训, 杨高波. 架构于抗混叠轮廓波的红外图像插值放大算法[J]. 光电工程, 2010, 37(6): 73. JIN Wei, YIN Cao-qian, ZHOU Ya-xun, YANG Gao-bo. An Infrared Image Interpolation Algorithm Using Aliasing-free Contourlet[J]. Opto-Electronic Engineering, 2010, 37(6): 73.