关键词: 图像超分辨率, 深度学习, 卷积神经网络, 特征转移

Abstract: Single Image Super-Rsolution（SISR）, which aims at obtaining a high-resolution image from a single low-resolution image, is a classical problem in computer vision. At present the convolution neural network reconstruction algorithm only has three layers. When dealing with real word data and internal structure is complex, it will appear problem of insufficient characterization ability. In order to meet the requirements of higher super-resolution reconstruction precision, an eight-layer structure is proposed by feature transfer. For different test datasets, the model of Transfer Learning Super-Resolution using Convolutional Neural Network（TLSRCNN） achieves good super-resolution results, and the subjective visual effect and objective evaluation indices are both improved obviously. Peak signal-to-noise ratio of TLSRCNN is the highest in contrast to other algorithms. The image resolution and edge sharpness are enhanced. Experimental results demonstrate TLSRCNN effectiveness of image super-resolution reconstruction, and network convergence speed is faster, which can recover image texture details better in comparison with traditional method.

Key words: image super-resolution, deep learning, convolution neural network, feature transfer