Abstract
Autonomous underwater vehicles (AUVs) are essential for marine exploration, monitoring, and surveillance, especially in hazardous or inaccessible environments for human divers. Underwater imaging systems frequently face considerable difficulties in detecting and tracking objects due to image degradation resulting from light scattering, colour distortion, and haze. Conventional enhancement methods-like histogram equalisation and gamma correction-struggle with non-uniform illumination and frequently do not maintain critical structural details and perceptual quality. To address these limitations, this work proposes a novel framework for underwater image dehazing and enhancement that incorporates three essential components: a generative adversarial network (GAN), a bottleneck attention module (BAM), and an enhanced Retinex-based contrast enhancement technique. The GAN acquires the intricate correspondence between deteriorated and high-quality underwater images, facilitating the restoration of fine textures and the attenuation of noise. The BAM selectively amplifies spatial and channel-specific features, thereby augmenting the network's capacity to preserve natural hues and intricate details. The modified Retinex algorithm adaptively distinguishes between illumination and reflectance components, facilitating context-sensitive contrast enhancement across various lighting conditions. This integrated architecture facilitates collaborative learning among generative modelling, attention-driven feature refinement, and physics-based enhancement. The proposed method undergoes thorough evaluation on the underwater Image enhancement benchmark (UIEB) dataset, which consists of 890 authentic underwater images. This study presents exceptional quantitative performance across various evaluation metrics: a UIQM score of 3.71 (indicating image quality), a PSNR of 28.4 dB (reflecting signal fidelity), an SSIM of 0.88 (representing structural similarity), and a perceptual LPIPS score of 0.082. The low LPIPS score underscores the perceptual realism of the enhanced images, correlating effectively with human visual preferences. These results distinctly surpass current classical and learning-based enhancement methods, demonstrating the efficacy and resilience of this approach for practical underwater vision applications.