Abstract:
Underwater environment offers exceptional challenges like color cast, contrast problems and
haze, hindering precise analysis. To increase visual perceptual quality of underwater images,
we introduce, Integrated Generative Adversarial Network (IGAN), a novel approach that uses a
combination of Generative Adversarial Networks (GANs), Spatial and Channel Attention and
U-Net Architecture. The integration of attention mechanisms within GAN and U-Net architecture
noticeably improves capability of model to effectively learn features essential for image
enhancement. Our proposed model outrivals other methods in capturing and enhancing complicated
details by concentrating on pertinent spatial and channel features. Firstly, we add dualattention
mechanism to U-Net encoder which accurately extracts features in different channels
and spatial dimensions. Secondly, we used Avg-TopK pooling in spatial attention which averages
top specified K pixels within a feature map to retain most salient features while reducing
dimensionality. Thirdly, one-sided label smoothing is used for better generalization and training
stability. Finally, we used Efficient Channel Attention (ECA) module which is a lightweight
attention mechanism that enhances feature representation by weighting each channel’s importance
using a fast 1D convolution to capture local inter-channel interactions, where kernel size
k denoting number of local inter-channel interactions, is adaptively selected. Comprehensive
qualitative and quantitative analysis across various datasets, which includes U45 dataset, UIEB
test dataset, UID dataset and UIEB challenge dataset, demonstrate extraordinary performance of
ix
IGAN in comparison with current state-of-the-art methods. In UIEB test dataset, IGAN achieves
outstanding PSNR (26.53) and SSIM (0.905) values, outperforming MuLA-GAN, second best
model, with values of 25.59 and 0.893, respectively representing 3.65% increase in PSNR and
1.34% improvement in SSIM. Experimental results illustrate that our proposed model surpasses
others in robustness, especially in enhancing underwater images with distortion, turbidity, and
various color shifts. Moreover, it also achieved optimal color accuracy and contrast levels to
desired level of human visual perception while improving detailed texture of the images. Our
proposed model surpasses other methods in capturing and enhancing intricate details by concentrating
on relevant spatial and channel features.
