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Author:

Li, Mengxing (Li, Mengxing.) | Wang, Suyu (Wang, Suyu.)

Indexed by:

EI Scopus

Abstract:

Low illumination image has the characteristics of low overall brightness, contrast and signal-to-noise ratio. The classical image enhancement algorithms have limited enhancement effect and need to adjust parameters manually. In this paper, a deep full convolutional coding-decoder based on U-type network is proposed to solve the problem of low illumination image degradation. The experimental results show that, compared with the existing mainstream image enhancement algorithms, the proposed algorithm can improve the brightness and contrast adaptively, avoid artifacts on image edges, and further improve the objective evaluation index and subjective evaluation. © 2020, Springer Nature Singapore Pte Ltd.

Keyword:

Convolution Image coding Computation theory Image enhancement Convolutional neural networks Signal to noise ratio Edge detection Luminance

Author Community:

  • [ 1 ] [Li, Mengxing]Beijing Engineering Research Center for IoT Software and Systems, Beijing, China
  • [ 2 ] [Li, Mengxing]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 3 ] [Wang, Suyu]Beijing Engineering Research Center for IoT Software and Systems, Beijing, China
  • [ 4 ] [Wang, Suyu]Faculty of Information Technology, Beijing University of Technology, Beijing, China

Reprint Author's Address:

  • [li, mengxing]beijing engineering research center for iot software and systems, beijing, china;;[li, mengxing]faculty of information technology, beijing university of technology, beijing, china

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Source :

ISSN: 1876-1100

Year: 2020

Volume: 551 LNEE

Page: 20-30

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 22

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