关键词: 超分辨率, 稀疏表示, 形态组成分析, 主成分分析, 颜色空间, 机器学习

Abstract: The traditional Super-Resolution (SR) algorithm, based on sparse dictionary pairs, is slow in training speed, poor in dictionary quality and low in feature matching accuracy. In view of these disadvantages, a super-resolution algorithm based on the improved sparse coding was proposed. In this algorithm, a Morphological Component Analysis (MCA) method with adaptive threshold was used to extract picture feature, and Principal Component Analysis (PCA) algorithm was employed to reduce the dimensionality of training sets. In this way, the effectiveness of the feature extraction was improved, the training time of dictionary was shortened and the over-fitting phenomenon was reduced. An improved sparse K-Singular Value Decomposition (K-SVD) algorithm was adopted to train low-resolution dictionary, and the super-resolution dictionary was solved by utilizing overlapping relation, which enforced the effectiveness and self-adaptability of the dictionary. Meanwhile, the training speed was greatly increased. Through the reconstruction of color images in the Lab color space, the degradation of the reconstructed image quality, which may be caused by the color channel's correlation, was avoided. Compared with traditional methods, this proposed approach can get better high-resolution images and higher computational efficiency.

Key words: super-resolution, sparse representation, Morphological Component Analysis (MCA), Principal Component Analysis (PCA), color space, machine learning