TY - GEN
T1 - Underwater Image Enhancement with Multi-Scale Residual Attention Network
AU - Ueki, Yosuke
AU - Ikehara, Masaaki
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Underwater images suffer from low contrast, color distortion and visibility degradation due to the light scattering and attenuation. Over the past few years, the importance of underwater image enhancement has increased because of ocean engineering and underwater robotics. Existing underwater image enhancement methods are based on various assumptions. However, it is almost impossible to define appropriate assumptions for underwater images due to the diversity of underwater images. Therefore, they are only effective for specific types of underwater images. Recently, underwater image enhancement algorisms using CNNs and GANS have been proposed, but they are not as advanced as other image processing methods due to the lack of suitable training data sets and the complexity of the issues. To solve the problems, we propose a novel underwater image enhancement method which combines the residual feature attention block and novel combination of multi-scale and multi-patch structure. Multi-patch network extracts local features to adjust to various underwater images which are often Non-homogeneous. In addition, our network includes multi-scale network which is often effective for image restoration. Experimental results show that our proposed method outperforms the conventional method for various types of images.
AB - Underwater images suffer from low contrast, color distortion and visibility degradation due to the light scattering and attenuation. Over the past few years, the importance of underwater image enhancement has increased because of ocean engineering and underwater robotics. Existing underwater image enhancement methods are based on various assumptions. However, it is almost impossible to define appropriate assumptions for underwater images due to the diversity of underwater images. Therefore, they are only effective for specific types of underwater images. Recently, underwater image enhancement algorisms using CNNs and GANS have been proposed, but they are not as advanced as other image processing methods due to the lack of suitable training data sets and the complexity of the issues. To solve the problems, we propose a novel underwater image enhancement method which combines the residual feature attention block and novel combination of multi-scale and multi-patch structure. Multi-patch network extracts local features to adjust to various underwater images which are often Non-homogeneous. In addition, our network includes multi-scale network which is often effective for image restoration. Experimental results show that our proposed method outperforms the conventional method for various types of images.
KW - Attention
KW - Image enhancement
KW - Image processing
KW - Image restoration
KW - Underwater image
UR - http://www.scopus.com/inward/record.url?scp=85125257910&partnerID=8YFLogxK
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U2 - 10.1109/VCIP53242.2021.9675342
DO - 10.1109/VCIP53242.2021.9675342
M3 - Conference contribution
AN - SCOPUS:85125257910
T3 - 2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Proceedings
BT - 2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Visual Communications and Image Processing, VCIP 2021
Y2 - 5 December 2021 through 8 December 2021
ER -