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

摘要：

图像超分辨率重建即使用特定算法将同一场景中的低分辨率模糊图像恢复成高分辨率图像。近年来，随着深度学习的蓬勃发展，该技术在很多领域都得到了广泛的应用，在图像超分辨率重建领域中基于深度学习的方法被研究的越来越多。为了掌握当前基于深度学习的图像超分辨率重建算法的发展状况和研究趋势，对目前图像超分辨率的流行算法进行综述。主要从现有单幅图像超分辨算法的网络模型结构、尺度放大方法和损失函数三个方面进行详细论述，分析各类方法的缺陷和益处，同时通过实验对比分析不同网络模型、不同损失函数在主流数据集上的重建效果，最后展望基于深度学习的单幅图像超分辨重建算法未来的发展方向。

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