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High-resolution multispectral (HRM) images are widely used in many remote sensing applications. Using the pan-sharpening technique, a low-resolution multispectral (LRM) image and a high-resolution panchromatic (HRP) image can be fused to an HRM image. This letter proposes a new compressed sensing (CS)-based pan-sharpening method which views the image observation model as a measurement process in the CS theory and constructs a joint dictionary from LRM and HRP images in which the HRM is sparse. The novel joint dictionary makes the method practical in fusing real remote sensing images, and a tradeoff parameter is added in the image observation model to improve the results. The proposed algorithm is tested on simulated and real IKONOS images, and it results in improved image quality compared to other well-known methods in terms of both objective measurements and visual evaluation.