Fusion of Panchromatic and Multispectral Images Using Multiscale Convolution Sparse Decomposition

In this article, we proposed a novel image fusion method based on multiscale convolution sparse decomposition (MCSD). A unified framework based on MCSD is first utilized to decompose panchromatic (PAN) image and the spatial component of upsampled low spatial resolution multispectral (LR MS) images, which can produce the corresponding low frequencies and feature maps. By combining convolution sparse decomposition with multiscale analysis, MCSD can efficiently approximate the spatial and spectral information in images. Next, a binary map generated from gradient information is utilized to integrate the low frequencies of LR MS and PAN images. For feature maps, the fusion gain for each pixel is calculated according to the similarity between the local patches from them. Finally, the fused image is reconstructed by the sum of fused low frequency and feature maps. Some experiments are conducted on QuickBird and GeoEye-1 satellite datasets. Compared with other methods, the proposed method performs better in visual and numerical evaluations.

Automated summary

A novel image fusion method based on multiscale convolution sparse decomposition (MCSD) is proposed in this article, which efficiently approximates the spatial and spectral information in images. By decomposing and integrating the components of panchromatic and multispectral images, the method performs better in visual and numerical evaluations compared to other methods.