A Super-resolution Reconstruction Method of Remotely Sensed Image Based on Sparse Representation

The traditional method of image super-resolution reconstruction uses the sub-pixel displacement information between multi-frame low-resolution images to reconstruct a high-resolution image. Image super-resolution reconstruction is a typical mathematical inverse problem, and it is ill-posed problem [1]. To solve this problem, prior knowledge of data or question should be added. As the latest development achievements of signal priori or modeling, sparse representation of the signal has been studied in depth in the field of image processing. Super-resolution reconstruction based on sparse representation can improve the image quality and get richer image details [8]. Due to the sparse representation of image reconstruction has strong priority, this paper focuses on super-resolution reconstruction of the single frame remotely sensed image based on sparse representation. Compared with other algorithms, it is proved that the super-resolution reconstruction algorithm based on sparse representation has advantages in remotely sensed image reconstruction