关键词: 图像超分辨率重建, 深度学习, 卷积神经网络, 密集残差学习, 反卷积

Abstract: Aiming at the problems of classical super-resolution reconstruction method based on convolutional neural network, such as shallow network, less extracted features and blurred image reconstruction, an improved single image super-resolution based on convolutional neural network is proposed. In this way, a novel deep convolutional neural network structure consisting of a dense residual network and a deconvolution network is designed. Firstly, the original low-resolution image input network uses the dense residual learning network to obtain richer effective features and accelerate the feature gradient flow. Secondly, the image features are up sampled to the target image size through the deconvolution layer, and then the dense residual learning is used to get high dimensional features. Finally, the features extracted by different convolution kernels are obtained to obtain the final reconstructed image. Experiments are performed on the Set5 and Set14 datasets and compared with classical reconstruction methods such as Bicubic, K-SVD, SelfEx and SRCNN. The reconstructed images are better in terms of overall sharpness and edge sharpness. In addition, the Peak Signal-to-Noise Ratio（PSNR） averaged by 2.69 dB, 1.68 dB, 0.74 dB and 0.61 dB, respectively. The experimental results show that the proposed method can obtain more detailed information, which get better visual effects and achieve the enhanced task of image super-resolution.

Key words: image super-resolution reconstruction, deep learning, convolutional neural network, dense residual learning, deconvolution