A novel variational model for pan-sharpening based on L1 regularization

Abstract

ABSTRACTThe Pan-sharpening process synthesizes a multispectral image of high spatial and high spectral quality by injecting the spatial details extracted from the high spatial resolution panchromatic image into the low-resolution multispectral image. In this letter, we propose a novel variational model for pan-sharpening based on L1 regularization, which consists of three energy terms. The first energy term extracts the geometric information from panchromatic image with exponential enhancement function, which can improve the contrast of the fused image by nonlinear expanding of gradient magnitude. The second energy term develops the low-pass filter based on the modulation transfer function (MTF) of the different MS sensor, which can preserve spectral information by determining the injected spatial details adaptively. Compared with the total variation regularization, the L1 regularization encourages the feature of sparse representation, which can improve the spatial fidelity of pan-sharpened images. In addition, introducing L1 regularization term into the variational framework can smooth the image noise and guarantee the stability of the numerical solution. Experimental results demonstrate that the proposed method outperforms several state-of-the-art pan-sharpening methods, including intensity-hue-saturation combined with Brovey transform (IHS-BT), additive wavelet luminance propotional (AWLP), total variation regularization (TVR) and MTF Contrast-based (MTF-CON) method.