关键词: 图像超分辨率, 深度学习, 卷积神经网络, 生成对抗网络

Abstract:

Image super-resolution reconstruction refers to the use of a specific algorithm to restore a low-resolution blurry image in the same scene to a high-resolution image. In recent years, with the active development of deep learning, this technology has been widely used in many fields, and methods based on deep learning are being increasingly studied in the field of image super-resolution reconstruction. In order to understand the current status and research trends of image super-resolution reconstruction algorithms based on deep learning, popular image super-resolution algorithms are summarized. Mainly, the network model structure, scaling method, loss function of existing single image super-resolution algorithm are explained in detail. The drawbacks and advantages of various methods are analyzed. The reconstruction effects of various network models and various loss functions are compared and analyzed throughout the experiment. Finally, the future development direction of the single-image super-resolution reconstruction algorithm based on deep learning is forecasted.

Key words: super-resolution reconstruction, deep learning, convolutional neural network, generative adversarial network