A Single Image Super Resolution Algorithm Based on Reciprocal Cell Model
ZHAO Liling;SUN Quansen;ZHANG Zelin;School of Computer Science and Technology, Nanjing University of Science and Technology;School of Information and Control Engineering, Nanjing University of Information Science and Technology;
In order to improve single image resolution, a new algorithm based on the reciprocal cell model is proposed. First, based on the framework of example-based learning super resolution theory by Freeman, a pre-filtering step is introduced by reciprocal cell model. Then a corresponding relationship is established, within the feature enhanced low resolution image and the original high resolution image. At last, the super-resolution reconstruction is completed by using the trained correspondence. The characteristics of the low resolution image are enhanced after the new pre-filtering algorithm. The problems of "one-to-many" and "dimension difference" in training database between the low and high resolution image feature space is effectively weakened. Comparing with other algorithms such as bi-cubic interpolation, neighbor embedding and the example-based learning algorithm, our experimental results show that the new approach has better effect on subject image quality and PSNR index.