Wu, Wei; Yang, Xiaomin; Yu, Yanmei; Shi, Yixing; He, Xiaohai Image super-resolution using KPLS. (Chinese. English summary) Zbl 1249.68300 J. Univ. Electron. Sci. Technol. China 40, No. 1, 105-110 (2011). Summary: A learning-based super-resolution algorithm based on kernel partial least squares (KPLS) regression is proposed. First, the KPLS regression algorithm is introduced. Then, the super-resolution algorithm based on KPLS regression is analyzed. High resolution images use high-frequency information as their features, while low resolution images use middle-frequency information as their features. Based on the relationship of the high and low resolution images, KPLS is used to set up a regression model. The regression model is applied to infer a high-resolution image. Experimental results show that our method can achieve very good results to face images and car plate images. The results of our method are closer to the real images. MSC: 68U10 Computing methodologies for image processing 62J02 General nonlinear regression Keywords:image restoration; kernel partial least squares method; super-resolution; regression algorithm PDFBibTeX XMLCite \textit{W. Wu} et al., J. Univ. Electron. Sci. Technol. China 40, No. 1, 105--110 (2011; Zbl 1249.68300) Full Text: DOI