Learning Interpolation via Regional Map for Pan-Sharpening

Although the bandwidth of the high-resolution panchromatic (HR PAN) image is wide, it is narrow in each band of the low-resolution multispectral (LR MS) image. Hence, the spatial resolution of the HR PAN image is much higher than that of the LR MS image. However, HR PAN image only has a single band. The purpose of the Pan-sharpening algorithm is to make the Pan-sharpened image with both high spatial resolution and good spectral information. In this paper, a novel learning interpolation method for Pan-sharpening is proposed by expanding the sketch information in the HR PAN image. The sketch information contains the edges and lines features of the image, and each segment of the sketch information has its own direction. According to the primal sketch graph of the HR PAN image, a regional map is obtained by a designed geometrical template. Since the size of the HR PAN image is different from that of the LR MS image, the LR MS image is interpolated into an interpolated multispectral (IMS) image by the nearest interpolation method. In addition, the IMS image can be mapped into the structure and the nonstructure regions by this regional map. The nonstructure regions are divided into the smooth and the texture regions by a variance value. For the structure and texture regions, the interpolated pixels in the IMS image are relearned and readjusted by the proposed structure and texture learning interpolation method, respectively. Experimental results show that the proposed Pan-sharpening method can provide superior performance in both visual effect and quality metrics, particularly for the images with a large spectral difference.