Video super-resolution reconstruction based on deep convolutional neural network and spatio-temporal similarity

Existing learning-based super-resolution(SR) reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames.Aiming at applying the advantages of learning-based algorithms to video SR field, a novel video SR reconstruction algorithm based on deep convolutional neural network(CNN) and spatio-temporal similarity(STCNN-SR) was proposed in this paper.It is a deep learning method for video SR reconstruction, which considers not only the mapping relationship among associated low-resolution(LR) and high-resolution(HR) image blocks, but also the spatio-temporal non-local complementary and redundant information between adjacent low-resolution video frames.The reconstruction speed can be improved obviously with the pre-trained end-to-end reconstructed coefficients.Moreover, the performance of video SR will be further improved by the optimization process with spatio-temporal similarity.Experimental results demonstrated that the proposed algorithm achieves a competitive SR quality on both subjective and objective evaluations, when compared to other state-of-the-art algorithms.