Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding

Underwater images suffer from color casts and low contrast due to wavelength-and distance-dependent attenuation and scattering. To solve these two degradation issues, we present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Concret...

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Vydané v:IEEE transactions on image processing Ročník 30; s. 1
Hlavní autori: Li, Chongyi, Anwar, Saeed, Hou, Junhui, Cong, Runmin, Guo, Chunle, Ren, Wenqi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Coders
Color
color correction
Decoding
Embedding
Feature extraction
Image color analysis
Image contrast
Image enhancement
Image quality
Image transmission
Imaging
Scattering
scattering removal
Underwater
underwater imaging
Visualization
Wave attenuation
ISSN:1057-7149, 1941-0042, 1941-0042
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Shrnutí:Underwater images suffer from color casts and low contrast due to wavelength-and distance-dependent attenuation and scattering. To solve these two degradation issues, we present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Concretely, we first propose a multi-color space encoder network, which enriches the diversity of feature representations by incorporating the characteristics of different color spaces into a unified structure. Coupled with an attention mechanism, the most discriminative features extracted from multiple color spaces are adaptively integrated and highlighted. Inspired by underwater imaging physical models, we design a medium transmission (indicating the percentage of the scene radiance reaching the camera)-guided decoder network to enhance the response of network towards quality-degraded regions. As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods. Extensive experiments demonstrate that our Ucolor achieves superior performance against state-of-the-art methods in terms of both visual quality and quantitative metrics. The code is publicly available at.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2021.3076367