Typical supervised image enhancement pipeline is to minimize the distance between the enhanced image and the reference one. Pixel-wise and perceptual-wise loss functions could help to improve the general image quality, however are not very efficient in improving the image aesthetic quality. In this paper, we propose a novel Residual connected Dilated U-Net (RDU-Net) for improving the image aesthetic quality. By using different dilation rates, the RDU-Net can extract multiple receptive-field features and merge the maximum information from local to global, which are highly desired in image enhancement. Also, we propose an encoder constraint perceptual loss, which could teach the enhancement network to dig out the latent aesthetic factors and make the enhanced image more natural and aesthetically appealing. The proposed approach can alleviate the over-enhancement phenomenons. The experimental results show that the proposed perceptual loss function could give a steady back propagation and the proposed method outperforms the state-of-the-arts.