关键词: 超分辨率, 遥感影像, 字典学习, 稀疏重建

Abstract: In order to enhance the spatial resolution of remote sensing images by software, this paper proposes a remote sensing image super-resolution reconstruction algorithm based on double-sparse K-SVD dictionary learning. Based on the sparse representation theory, the K-SVD dictionary learning algorithm is used to solve the low resolution dictionary and its sparse coefficient. The sparse coefficient passes to the high-resolution dictionary learning space to form high and low resolution dictionary pairs and reconstruct high-resolution remote sensing images. In the dictionary learning and sparse reconstruction stages, different degrees of sparseness are set. The experiments are respectively carried out, using TM5 image, resource No.3 image and remote sensing image in USC_SIPI image library to reconstruct. The results show that the regardless of whether the reconstructed image is noisy, the peak signal-to-noise ratio and similarity of the algorithm are higher than those of Bicubic and Zeyde. The introduction of K-SVD and double-sparse parameters not only reduces dictionary learning time, but also has high enhancement capability of spatial resolution.

Key words: super-resolution, remote sensing image, dictionary learning, sparse reconstruction