Learning-based image super-resolution using weight coefficients of synaptic connections

The new learning-based image super-resolution method is described in this article. The process of increasing the resolution of video frames or images from a set according to the method is based on the weight coefficients of synaptic connections. These coefficients are obtained by the learning neural-like structure on a pair of images of low and high resolution. The dimension influence of the training set on the generalization properties of the neural-like structure is investigated. The comparison of work effectiveness of the proposed method to existing ones is analyzed.