Fusion of multispectral and panchromatic images via convolutional sparse representation and morphological filter

Aimed at the lack of the spectral information preservation and the spatial detail injection in fusion of multispectral (MS) and panchromatic (PAN) images, the paper proposed a pansharpening algorithm based on convolutional sparse representation (CSR) and morphological filter (MF) by introducing a recently emerged signal decomposition model known as CSR. Firstly, the PAN and MS images are decomposed to obtain a base layer and a detail layer, respectively. Secondly, the fusion rule of the base layers which based on MF and high-pass modulation (HPM) scheme is proposed to retain more details. For the fusion of detail layers, maximum selection scheme based on activity maps and CSR model are adopted for fusion. Finally, the fusion results of the base layer and detail layer are reconstructed to obtain the final fusion image. The experimental results show that the proposed method is superior to the traditional methods and some current popular fusion methods from the visual effects and the objective indices.