Efficient Super Resolution by Recursive Aggregation | |
Luo, Zhengxiong1,2,3,4; Huang, Yan2,3,4; Li, Shang1,4; Wang, Liang2,3,5,6; Tan, Tieniu2,3,6 | |
2021-01 | |
会议名称 | International Conference on Pattern Recognition (ICPR) |
会议日期 | 2021-1 |
会议地点 | 意大利米兰 |
出版者 | IEEE |
摘要 | Deep neural networks have achieved remarkable results on image super-resolution (SR), but the efficiency problem of deep SR networks is rarely studied. We experimentally find that many sequentially stacked convolutional blocks in nowadays SR networks are far from being fully optimized, which largely damages their overall efficiency. It indicates that comparable or even better results could be achieved with less but sufficiently optimized blocks. In this paper, we try to construct more efficient SR model via the proposed recursive aggregation network (RAN). It recursively aggregates convolutional blocks in different orders, and avoids too many sequentially stacked blocks. In this way, multiple shortcuts are introduced in RAN, and help gradients easier flow to all inner layers, even for very deep SR networks. As a result, all blocks in RAN can be better optimized, thus RAN can achieve better performance with smaller model size than existing methods. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51940 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Huang, Yan |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS) 2.Center for Research on Intelligent Perception and Computing (CRIPAC) 3.National Laboratory of Pattern Recognition (NLPR) 4.Institute of Automation, Chinese Academy of Sciences (CASIA) 5.Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) 6.Chinese Academy of Sciences, Artificial Intelligence Research (CAS-AIR) |
第一作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
通讯作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Luo, Zhengxiong,Huang, Yan,Li, Shang,et al. Efficient Super Resolution by Recursive Aggregation[C]:IEEE,2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
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