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题名: Ensemble based deep networks for image super-resolution
作者: Wang Lingfeng(汪凌峰)1; Huang, Zehao1; Gong Yongchao(宫永超)1; Pan Chunhong(潘春洪)1
刊名: PATTERN RECOGNITION
出版日期: 2017-08-01
卷号: 68, 期号:null, 页码:191-198
关键词: Super-resolution ; Ensemble ; Sparse prior ; Deep networks
DOI: 10.1016/j.patcog.2017.02.027
通讯作者: Wang, Lingfeng
文章类型: Article
英文摘要: There have been significant advances in deep learning based single-image super-resolution (SISR) recently. With the advantage of deep neural networks, deep learning based methods can learn the mapping from low-resolution (LR) space to high-resolution (HR) space in an end-to-end manner. However, most of them only use a single model to generate HR result. This brings two drawbacks: (1) the risk of getting stuck in local optima and (2) the limited representational ability of single model when handling various input LR images. To overcome these problems, we novelly suggest a general way through introducing the idea of ensemble into SR task. Furthermore, instead of simple averaging, we propose a back-projection method to determine the weights of different models adaptively. In this paper, we focus on sparse coding network and propose ensemble based sparse coding network (ESCN). Through the combination of multiple models, our ESCN can generate more robust reconstructed results and achieve state-of-the-art performance. (C) 2017 Elsevier Ltd. All rights reserved.
WOS标题词: Science & Technology ; Technology
类目[WOS]: Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]: Computer Science ; Engineering
收录类别: SCI
项目资助者: National Natural Science Foundation of China(61403376 ; 91646207 ; 61370039)
语种: 英语
WOS记录号: WOS:000401381100014
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ia.ac.cn/handle/173211/14495
Appears in Collections:模式识别国家重点实验室_遥感图像处理团队_期刊论文

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作者单位: 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China

Recommended Citation:
Wang, Lingfeng,Huang, Zehao,Gong, Yongchao,et al. Ensemble based deep networks for image super-resolution[J]. PATTERN RECOGNITION,2017,68(null):191–198.
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