基于引导滤波和shearlet稀疏的遥感图像融合算法

针对遥感图像空间分辨率和光谱分辨率不可兼得的情况，结合多尺度变换与稀疏表示，提出一种shearlet稀疏基与引导滤波共同作用的遥感图像融合算法。以IHS融合模型为基础，利用引导滤波作拟合处理，再用shearlet变换分解亮度图像和全色图像，得到图像的高低频子带系数。对低频子图进行稀疏化处理并获取最优稀疏系数，稀疏系数以图像块活跃度取大的标准进行替换融合。基于区域能量和区域方差融合处理对应的高频子图，再利用shearlet反变换获取融合结果。实验结果表明，本文算法能提高图像清晰度以及光谱保留度，在图像完整度和细节考量上远好于其他算法。

A remote sensing image fusion algorithm based on guided filtering and shearlet sparse base

For the situation that the spatial resolution and spectral resolution of remote sensing images cannot be combined, we propose a remote sensing image fusion algorithm based on shearlet sparse base and guided filtering by combing multi scale transform with sparse representation. Based on the IHS fusion model, we adopt the guided filtering for the fitting process. Then the brightness image and the panchromatic image are decomposed by the shearlet transform to obtain the high and low frequency sub band coefficients of the image. The low frequency sub images are sparsely processed and the optimal sparse coefficients are obtained, and fusion is performed based on the criterion that the activity degree of image blocks is large. The corresponding high frequency sub images are fused based on regional energy and regional variance and obtain the fusion results via the shearlet inverse transformation. Experimental results show that the proposed algorithm can improve image sharpness and spectral retention, and it outperforms other algorithms in image integrity and detail.