Abstract: 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.

Key words: example-based learning, reciprocal cell, dimension difference, super resolution