Super-resolution by GMM based conversion using self-reduction image
Yuki Ogawa, Yasuo Ariki, Tetsuya Takiguchi · 2012
In recent years, super-resolution techniques in the field of computer vision have been studied actively owing to the potential applicability in various fields. In this paper, we propose a single-image, super-resolution approach using GMM (Gaussian Mixture Model)-based conversion. The conversion function is constructed by GMM 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 the experiments.