Abstract
ABSTRACT Underwater image enhancement and processing have gained prominence in the field of image processing due to marine scientists' interest in uncovering new species and environments. This work presented a lightweight, attention-based, deep hybrid convolutional neural network (LW-AB-DHCNN) architecture to enhance overall efficiency. The traditional CNNs use subnetworks to enhance model depth and achieve the same functionality, but the proposed method employs multiple depth-wise separable convolutions, thereby reducing the computational complexity of the system. The proposed approach integrates Deep CNN with CBAM to provide an enhanced U-Net model. CBAM employs a self-attention method to acquire both local and global data in underwater images, thereby augmenting their semantic interpretation. This work also employed a unified error function to direct the training and optimization of the model. When the presented scheme was evaluated against benchmark datasets, it achieved an average PSNR of 25.69 dB and an average SSIM of 0.8624.
Published Version
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