计算机科学
人工智能
编码(集合论)
图像(数学)
块(置换群论)
深度学习
循环神经网络
人工神经网络
迭代重建
计算机视觉
机器学习
模式识别(心理学)
几何学
数学
集合(抽象数据类型)
程序设计语言
作者
Zhen Li,Jinglei Yang,Zheng Liu,Xiaobo Yang,Gwanggil Jeon,Wei Wu
标识
DOI:10.1109/cvpr.2019.00399
摘要
Recent advances in image super-resolution (SR) explored the power of deep learning to achieve a better reconstruction performance. However, the feedback mechanism, which commonly exists in human visual system, has not been fully exploited in existing deep learning based image SR methods. In this paper, we propose an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information. Specifically, we use hidden states in a recurrent neural network (RNN) with constraints to achieve such feedback manner. A feedback block is designed to handle the feedback connections and to generate powerful high-level representations. The proposed SRFBN comes with a strong early reconstruction ability and can create the final high-resolution image step by step. In addition, we introduce a curriculum learning strategy to make the network well suitable for more complicated tasks, where the low-resolution images are corrupted by multiple types of degradation. Extensive experimental results demonstrate the superiority of the proposed SRFBN in comparison with the state-of-the-art methods. Code is avaliable at https://github.com/Paper99/SRFBN_CVPR19.
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