Multiple Adverse Weather Removal Using Masked-Based Pre-Training and Dual-Pooling Adaptive Convolution

Multiple Adverse Weather Removal Using Masked-Based Pre-Training and Dual-Pooling Adaptive Convolution

Removing artifacts caused by multiple adverse weather, including rain, fog, and snow, is crucial for image processing in outdoor environments. Conventional high-performing methods face challenges, such as requiring pre-specification of weather types and slow processing times. In this study, we propo...

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Bibliographic Details
Main Authors: Shugo Yamashita, Masaaki Ikehara
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Convolutional neural network
dehazing
deraining
desnowing
large-scale pre-training
raindrop removal
Online Access:https://ieeexplore.ieee.org/document/10506517/

Internet

https://ieeexplore.ieee.org/document/10506517/

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