关键词: 稀疏表示, 超分辨率, 样本聚类, 字典对, 字典训练

Abstract: Recently, double dictionary training has emerged as a powerful tool for solving a class of super-resolution reconstruction problems in computer vision and image processing. To reduce the reconstruction error of dictionary training and reconstruction within image super-resolution reconstruction algorithm via double dictionary training, this paper proposes a super-resolution reconstruction algorithm based on adaptive multiple dictionary pairs (AMDP). This algorithm is available for different types of input images by samples clustering and training features dictionary. In dictionary training, differences among different dictionaries training are used to filter high frequency patches and reconstruct images repetitiously in restoration, which effectively improves the quality of reconstructed images. The experiments show that the proposed algorithm achieves improvement in image quality and provides more details.

Key words: sparse representation, super-resolution, sample cluster, dictionary pair, dictionary training