CFENet: Cost-effective underwater image enhancement network via cascaded feature extraction-Reference-Cited by-同舟云学术

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CFENet: Cost-effective underwater image enhancement network via cascaded feature extraction

Published:2024-07 Issue: Volume:133 Page:108561
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Ji Xun^ORCID,Wang Xu,Hao Li-Ying^ORCID,Cai Cheng-Tao

Publisher

Elsevier BV

Reference72 articles.

1. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions;Alzubaidi;J. Big Data,2021

2. Color balance and fusion for underwater image enhancement;Ancuti;IEEE Trans. Image Process.,2018

3. GCNet: Non-local networks meet squeeze-excitation networks and beyond;Cao,2019

4. MFFN: An underwater sensing scene image enhancement method based on multiscale feature fusion network;Chen;IEEE Trans. Geosci. Remote Sens.,2022

5. Perceptual underwater image enhancement with deep learning and physical priors;Chen;IEEE Trans. Circuits Syst. Video Technol.,2021

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