In order to get better image resolution enhancement performance

a image super-resolution algorithm is proposed mainly based on convolutional neural networks（ CNN）. Specifically

after normalizing the input image

a deep neural network is constructed and then trained by using residual learning and removing the redundant batch normalization（ BN） layers. The proposed method is evaluated by subjective and objective experiments

which proves its superiority over other traditional counterparts.