Generative adversarial networks with multi-scale and attention mechanisms for underwater image enhancement

Abstract

The images captured underwater are usually degraded due to the effects of light absorption and scattering. Degraded underwater images exhibit color distortion, low contrast, and blurred details, which in turn reduce the accuracy of marine biological monitoring and underwater object detection. To address this issue, a generative adversarial network with multi-scale and an attention mechanism is proposed to improve the quality of underwater images. To extract more effective features within the generative network, several modules are introduced: a multi-scale dilated convolution module, a novel attention module, and a residual module. These modules are utilized to design a generative network with a U-shaped structure. The multi-scale dilated convolution module is designed to extract features at multiple scales and expand the receptive field to capture more global information. The attention module directs the network’s focus towards important features, thereby reducing the interference from redundant feature information. To improve the discriminative power of the adversarial network, a multi-scale discriminator is designed. It has two output feature maps with different scales. Additionally, an improved loss function for the generative adversarial network is proposed. This improvement involves incorporating the total variation loss into the traditional loss function. The performance of different methods for enhancing underwater images is evaluated using the EUVP dataset and UIEB dataset. The experimental results demonstrate that the enhanced underwater images exhibit better quality and visual effects compared to other methods.

Lightweight underwater image adaptive enhancement based on zero-reference parameter estimation network

“…Moreover, they contributed to the EUVP dataset, which includes a collection of paired and unpaired underwater images. (Wang et al, 2023) proposed a generative adversarial network with multi-scale and attention mechanisms, which introduces multi-scale dilated convolution and directs the network's focus towards important features, thus reducing the interference from redundant feature information. (Huang et al, 2023) introduced a Zero-Reference Deep Network that is designed based on the classical haze image formation principle, aiming to explore zero-reference learning for underwater image enhancement.…”