AL GAN: A lightweight GAN underwater image enhancement incorporating attention mechanisms

DOI:	10.11809/bqzbgcxb2024.02.017
Keywords:	underwater image enhancement; attention mechanism; lightweight; generation of countermeasure network
Abstract:	Aiming at the problems of low contrast, color distortion and slow reasoning speed of existing network models in underwater images, this paper proposes a lightweight GAN underwater image enhancement model integrating attention mechanism. This model uses PatchGAN as the discriminant network to generate a network based on the Funi Gan model. MobileNet is used to replace the VGG16 model with a large number of parameters in the original U Net feature extraction network as the feature extraction module to extract features of underwater degraded images, thus reducing the number of parameters in the network model. The inference speed of network model is accelerated. Furthermore, channel and spatial attention mechanism are introduced into the feature extraction module to enhance the feature extraction ability of the network and achieve the purpose of enhancing image details. Experiments on EUVP data show that the proposed method is effective in processing real underwater images. Compared with several existing methods, the proposed method can improve contrast better, correct color bias, and reduce the loss of image detail information, and is superior to the existing methods in both subjective and objective indexes.
Published:	2024-02-28
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