Multi-modal Channel-attention for Vehicle RGB Image Reconstruction-Reference-Cited by-同舟云学术

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Multi-modal Channel-attention for Vehicle RGB Image Reconstruction

Published:2024-05-31 Issue: Volume: Page:57-62
ISSN:
Container-title:2024 International Conference on Cloud and Network Computing (ICCNC)
language:
Short-container-title:

Author:

Deng Qian¹,Li Sihan²,Yang Lingchen²,Liu Wenping³,Peng Ruiqing³,Xie Xiao⁴

Affiliation:

1. Hubei University of Economics,School of Information Management,Wuhan,China

2. Hubei University of Economics,School of Information Engineering,Wuhan,China

3. Hubei University of Economics,School of Information Management Institute of Digital Economy,Wuhan,China

4. Chinese Academy of Sciences,Key Laboratory for Environment Computation & Sustainability of Liaoning Province Institute of Applied Ecology,Shenyang,China

Funder

National Natural Science Foundation of China

Nova

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10608143/10608197/10608333.pdf?arnumber=10608333

Reference14 articles.

1. Multi-exposure image fusion via deep perceptual enhancement

2. Rethinking multi-exposure image fusion with extreme and diverse exposure levels: A robust framework based on Fourier transform and contrastive learning

3. TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework Using Self-Supervised Multi-Task Learning

4. Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation

5. Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity

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