Super-resolution Reconstruction of Remote Sensing Images by Using Empirical Mode Decomposition and Compressed Sensing

Super resolution (SR) of remote sensing images is significant for improving accuracy of target identification and for image fusing.Conventional fusion-based methods inevitably result in distortion of spectral information，a feasible solution to the problem is the single-image based super resolution.In this work，we proposed a single-image based approach to super resolution of multiband remote sensing images.The method combines the EMD (Empirical Mode Decomposition)，compressed sensing and PCA to dictionary learning and super resolution reconstruction of remote sensing color image.First，the original image is decomposed into a series of IMFs(Intrinsic Mode Function) according to their frequency component by using EMD，and the super resolution is implemented only on IMF1，which includes high-frequency component；then the K-SVD algorithm is used to learn and obtain overcomplete dictionaries，and the MOP (Orthogonal Matching Pursuit) algorithm is used to reconstruct the IMF1；Finally，the up-scaled IMF1 is combined with other IMFs to acquire the super resolution of original image.For a multiband image reconstruction，a PCA transform is first implemented on multiband image，and the PC1 is adopted for learning to get overcomplete dictionaries，the obtained dictionaries is then used to super-resolution reconstruction of each multi-spectral band.The Geoeye-1 panchromatic and multi-spectral images are used as experimental data to demonstrate the effectiveness of the proposed algorithm.The results show that the proposed method is workable to exhibit the detail within the images.