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
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
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