ReX-Net: A reflectance-guided underwater image enhancement network for extreme scenarios
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Dosyalar
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Due to the complex underwater environment, underwater images exhibit different degradation characteristics, severely affecting their practical applications. Although underwater image enhancement networks with physical priors exist, the statistical priors are not applicable in extreme underwater scenes. Therefore, we propose ReXNet, a reflectance-guided underwater image enhancement network for extreme scenarios. ReX-Net leverages the complementary information of reflectance and the underwater image obtained through the original encoder and reflectance encoder to minimize the impact of different scene environments. As underwater images contain object information at different scales, the encoder includes a TriFuse reflected-image object extractor module (TRIOE), which employs Tri-scale convolutions to capture features at different scales and utilize attention mechanisms to enhance channel and spatial information. In the decoder, we design a context sensitive multi-level integration module (CSMLI) to fuse feature vectors at different resolutions, thereby improving the expressiveness and robustness of features while avoiding artifacts and ensuring pixel accuracy. Experiments on multiple datasets demonstrate that ReX-Net outperforms existing methods. Furthermore, application experiments show the practicality of ReX-Net in other visualization tasks.
Açıklama
This work was supported in part by the National Natural Science Foundation of China (No. 61702074) , the Liaoning Provincial Natural Science Foundation of China (No. 20170520196) , the Fundamental Research Funds for the Central Universities, China (Nos. 3132019205 and 3132019354) , and the Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University.
Anahtar Kelimeler
Underwater Image Enhancement, Retinex Theory, UNet, Image Prior, Quality Assessment, Visibility
Kaynak
Expert Systems with Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
231
Sayı
Künye
Zhang, D., Zhou, J., Zhang, W., Lin, Z., Yao, J., Polat, K., ... & Alhudhaif, A. (2023). ReX-Net: A reflectance-guided underwater image enhancement network for extreme scenarios. Expert Systems with Applications, 120842.
