This paper studies the remote sensing image Pan-Sharpening problem from the perspective of compressed sensing (CS)theory,which ensures that with the sparsity regularization (minimization problem of l0 -norm ).We can describe a Pan-Sharpening problem asfollows:a low resolution multi-spectral image and a high resolution panchromatic image can be fused to obtain a high resolution multi-spectralimage.By using the CS theory,a model of Pan-Sharpening problem is constructed in our paper.Then we propose the algorithm based on theDiscrete Cosine Transform (DCT)matrix.As a dictionary,the standard DCT matrix has been used to solve the Pan-Sharpening problem inthe algorithm,we use some post-processing algorithms to deal with blocking artifact,color bias and washing effect problems.The proposedalgorithm are tested on QuickBird images with other classic algorithms.The result shows that compared to others algorithms,the proposedalgorithms outperforms in effects.%Pan-Sharpening问题是将高分辨率全色图像和低分辨率多光谱图像融合，重构出高分辨率多光谱图像的过程。压缩传感理论解决了相应目标函数的稀疏解问题（求解含有l0范数的极小化问题）。利用压缩传感理论，提出一种新的解决Pan-Sharpening问题的算法：以离散余弦变换矩阵作为字典进行重构。然后针对重构过程中产生的块效应、色彩偏差和洗涤效应等问题进行相应的处理。QuickBird卫星图像的实验结果表明：与经典算法进行对比，该算法在效果上有一定的优势。
展开▼
