Low-Light Image Enhancement Based on Multi-Path Interaction.

color channel convolutional neural network low-light image multi-path interaction

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
22 Jul 2021
Historique:
received: 22 06 2021
revised: 16 07 2021
accepted: 19 07 2021
entrez: 10 8 2021
pubmed: 11 8 2021
medline: 12 8 2021
Statut: epublish

Résumé

Due to the non-uniform illumination conditions, images captured by sensors often suffer from uneven brightness, low contrast and noise. In order to improve the quality of the image, in this paper, a multi-path interaction network is proposed to enhance the R, G, B channels, and then the three channels are combined into the color image and further adjusted in detail. In the multi-path interaction network, the feature maps in several encoding-decoding subnetworks are used to exchange information across paths, while a high-resolution path is retained to enrich the feature representation. Meanwhile, in order to avoid the possible unnatural results caused by the separation of the R, G, B channels, the output of the multi-path interaction network is corrected in detail to obtain the final enhancement results. Experimental results show that the proposed method can effectively improve the visual quality of low-light images, and the performance is better than the state-of-the-art methods.

Identifiants

pubmed: 34372222
pii: s21154986
doi: 10.3390/s21154986
pmc: PMC8347206
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Tianjin Intelligent Security Industry Chain Technology Adaptation and Application Project
ID : 18ZXZNGX00320

Références

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pubmed: 929159
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pubmed: 28113318
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pubmed: 15376593
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IEEE Trans Image Process. 2021;30:2340-2349
pubmed: 33481709

Auteurs

Bai Zhao (B)

School of Microelectronics, Tianjin University, Tianjin 300072, China.

Xiaolin Gong (X)

School of Microelectronics, Tianjin University, Tianjin 300072, China.

Jian Wang (J)

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
National Ocean Technology Center, Tianjin 300112, China.

Lingchao Zhao (L)

School of Microelectronics, Tianjin University, Tianjin 300072, China.

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Classifications MeSH