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author:

Du, X. (Du, X..) [1] | Xia, Y. (Xia, Y..) [2]

Indexed by:

Scopus

Abstract:

Variational Retinex model-based methods for low-light image enhancement have been popularly studied in recent years. In this paper, we present an enhanced variational Retinex method for low-light natural image enhancement, based on the initial smoother illumination component with a structure extraction technique. The Bergman splitting algorithm is then introduced to estimate the illuminance component and reflectance component. The de-block processing and illuminance component correction are used for the enhanced reflectance as the ultimate enhanced image. Moreover, the estimated smoother illumination component can make enhanced images preserve edge details. Experimental results with a comparison demonstrate the present variational Retinex method can effectively enhance image quality and maintain image color. © 2020, Springer Nature Switzerland AG.

Keyword:

Image enhancement; Structure extraction; Variational Retinex

Community:

  • [ 1 ] [Du, X.]Department of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
  • [ 2 ] [Xia, Y.]Department of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

Reprint 's Address:

  • [Xia, Y.]Department of Mathematics and Computer Science, Fuzhou UniversityChina

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Source :

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISSN: 0302-9743

Year: 2020

Volume: 12002 LNCS

Page: 408-420

Language: English

0 . 4 0 2

JCR@2005

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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