High-Dimensional Attention Generative Adversarial Network Framework for Underwater Image Enhancement

In recent years, underwater image enhancement (UIE) processing technology has developed rapidly, and underwater optical imaging technology has shown great advantages in the intelligent operation of underwater robots. In underwater environments, light absorption and scattering often cause seabed images to be blurry and distorted in color. Therefore, acquiring high-definition underwater imagery with superior quality holds essential significance for advancing the exploration and development of marine resources. In order to resolve the problems associated with chromatic aberration, insufficient exposure, and blurring in underwater images, a high-dimensional attention generative adversarial network framework for underwater image enhancement (HDAGAN) is proposed. The introduced method is composed of a generator and a discriminator. The generator comprises an encoder and a decoder. In the encoder, a channel attention residual module (CARM) is designed to capture both semantic features and contextual details from visual data, incorporating multi-scale feature extraction layers and multi-scale feature fusion layers. Furthermore, in the decoder, to refine the feature representation of latent vectors for detail recovery, a strengthen–operate–subtract module (SOSM) is introduced to strengthen the model’s capability to comprehend the picture’s geometric structure and semantic information. Additionally, in the discriminator, a multi-scale feature discrimination module (MFDM) is proposed, which aids in achieving more precise discrimination. Experimental findings demonstrate that the novel approach significantly outperforms state-of-the-art UIE techniques, delivering enhanced outcomes with higher visual appeal.