Manifold regularized sparse representation of injected details for pansharpening

Pansharpening with sparse representation (SR) and details injection (ID) can both produce visually and quantitatively pleasing images. The former constructs pansharpened image by combining the dictionary and estimated sparse coefficients while the later by sharpening the multispectral bands through adding the proper spatial details from panchromatic (Pan) image. The combination of these two methods has been putting forward as the pansharpening method based on sparse representation of injected details (SR-D). Although SR-D has achieved better results both in visual and quantitative parts than many state-of-art methods, it ignores the intrinsic geometric structure connection between the multispectral image (MS) and the corresponding high-resolution MS image. In this paper, we propose a new pansharpening method, called manifold regularized sparse representation of injected details (MR-SR-D) by introducing a manifold regularization (MR) into the former SR-D model. The manifold regularization utilized a graph Laplacian to incorporate the locally geometrical structure of the multispectral data. Experimental results on the IKONOS, QuickBird and WorldView2 data sets show that the proposed method can achieve remarkable spectral and spatial quality on both reduced scale and full scale.