TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Fast and Accurate Single Image Super- Resolution via Enhanced U-Net |
| 영문제목(English Title) |
Fast and Accurate Single Image Super- Resolution via Enhanced U-Net |
| 저자(Author) |
Le Chang
Fan Zhang
Biao Li
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| 원문수록처(Citation) |
VOL 15 NO. 4 PP. 1246 ~ 1262 (2021. 04) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Recent studies have demonstrated the strong ability of deep convolutional neural networks (CNNs) to significantly boost the performance in single image super-resolution (SISR). The key concern is how to efficiently recover and utilize diverse information frequencies across multiple network layers, which is crucial to satisfying super-resolution image reconstructions. Hence, previous work made great efforts to potently incorporate hierarchical frequencies through various sophisticated architectures. Nevertheless, economical SISR also requires a capable structure design to balance between restoration accuracy and computational complexity, which is still a challenge for existing techniques. In this paper, we tackle this problem by proposing a competent architecture called Enhanced U-Net Network (EUN), which can yield ready-to-use features in miscellaneous frequencies and combine them comprehensively. In particular, the proposed building block for EUN is enhanced from U-Net, which can extract abundant information via multiple skip concatenations. The network configuration allows the pipeline to propagate information from lower layers to higher ones. Meanwhile, the block itself is committed to growing quite deep in layers, which empowers different types of information to spring from a single block. Furthermore, due to its strong advantage in distilling effective information, promising results are guaranteed with comparatively fewer filters. Comprehensive experiments manifest our model can achieve favorable performance over that of state-of-the-art methods, especially in terms of computational efficiency.
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| 키워드(Keyword) |
Single Image Super-resolution
Convolutional neural networks
information propagation
U-Net Block
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| 파일첨부 |
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