非下采样Shearlet变换耦合边缘制约的遥感图像融合算法

为了解决当前遥感图像融合算法因忽略了区域中像素点的边缘特征而导致融合图像中存在块效应以及模糊效应的不足,在非下采样Shearlet变换的基础上,设计了基于边缘制约模型的遥感图像融合算法。首先,将多光谱(MS)图像经过IHS分解,提取相应的亮度分量。然后,通过非下采样Shearlet变换,将全色(PAN)图像与亮度分量进行分解,获取各自的高频系数与低频系数。再通过图像的空间频率特征,建立低频系数的融合函数,对低频系数进行融合。并利用图像的区域平均梯度特征与图像区域中像素点的边缘能量特征,构造了边缘制约模型,对高频系数进行融合。最后,将融合后低频系数、高频系数经非下采样Shearlet逆变换和IHS逆变换,获取融合图像。实验结果显示,与当前遥感图像融合方法相比,所提算法的融合图像具有更高的清晰度,更好地保持了图像的光谱特性,消除了块效应以及模糊效应。

Remote sensing image fusion algorithm using nonsubsampled shearlet transform and edge constraint model

In order to solve the problem of blocking effect and blurring effect induced by ignoring the edge features of image region pixels t in current remote sensing image fusion algorithms. the remote sensing image fusion algorithm based on edge constraint model and nonsubsampled shearlet transform was proposed in this paper. Firstly, multispectral images are decomposed by luminance hue saturation decomposition to extract luminance components. Then, the panchromatic image and luminance components are decomposed by nonsubsampled Shearlet transform to obtain high frequency coefficients and low frequency coefficients. Finally, the fusion function of low frequency coefficients is established through the spatial frequency characteristics of the image to fuse the low frequency coefficients. An edge constraint model is constructed to fuse the high frequency coefficients by using the average gradient feature and the edge energy feature of pixels in the image region. After the fusion, the low frequency coefficients and high frequency coefficients are inversely transformed by non downsampling Shearlet, then the fused images are obtained by inverse transform of IHS. The experimental results show that, compared with the current remote sensing image fusion methods, the fusion images designed in this paper not only have better clarity, but also have better spectral characteristics without blocking effect and blurring effect.