Image Super-resolution Based on Deep Learning Features.

Learning-based image super-resolution method is a research hotspot in recent years which uses prior knowledge of sample to reconstruct the image and has obvious advantages over other reconstruction methods. In this paper, we first analyze the factors of reconstructed image quality. Then we use randomized rectified linear unit (RReLU) to solve the problem of over compression in the original network. Besides, Nesterov0s accelerated gradient (NAG) is invoked to accelerate convergence and avoid large oscillations. Finally, we conduct a quantitative experiments to prove the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]