Super-resolution Using GMM and PLS Regression

Abstract

In recent years, super-resolution techniques in the field of computer vision have been studied in earnest owing to the potential applicability of such technology in a variety of fields. In this paper, we propose a single-image, super-resolution approach using a Gaussian Mixture Model (GMM) and Partial Least Squares (PLS) regression. A GMM-based super-resolution technique is shown to be more efficient than previously known techniques, such as sparse-coding-based techniques. But the GMM-based conversion may result in over fitting. In this paper, an effective technique for preventing over fitting, which combines PLS regression with a GMM, is proposed. The conversion function is constructed using the input image and its self-reduction image. The high-resolution image is obtained by applying the conversion function to the enlarged input image without any outside database. We confirmed the effectiveness of this proposed method through our experiments.