Super-resolution Method based on CS and Structural Self-similarity for Remote Sensing Images

A super resolution (SR) method for remote sensing images based on CS, structural self-similarity and dictionary learning is proposed. The basic idea is to find a dictionary that can represent the high resolution (HR) image sparsely. The extra information comes from the structural self-similarity that widely exist in remote sensing images and this kind of information can be learned through dictionary learning in the CS frame. In this method, we use K-SVD method to find the dictionary and OMP method to reveal the sparse representation coefficients. Compared with the traditional sample-based SR methods, the most difference of this method is that we only use the inputted low resolution image and its interpolation image rather than other HR images. In addition, an index called as structural self-similarity (SSSIM) is proposed here to evaluate the extent of structural self-similarity in the image. Results of some comparative experiments show that this proposed method has better SR effect and time efficiency, and the SSSIM index has strong correlation with the SR effect.