Underwater image enhancement based on multiscale fusion generative adversarial network

The underwater optical imaging environment presents unique challenges due to its complexity. This paper addresses the limitations of existing algorithms in handling underwater images captured in artificial light scenes. We proposed an underwater artificial light optimization algorithm to preprocess images with uneven lighting, mitigating the effects of light distortion. Furthermore, we proposed a novel underwater image enhancement algorithm based the Multiscale Fusion Generative Adversarial Network, named UMSGAN, to address the issues of low contrast and color distortion. UMSGAN uses the generative adversarial network as the underlying framework and first extracts information from the degraded image through three parallel branches separately, and adds residual dense blocks in each branch to learn deeper features. Subsequently, the features extracted from the three branches are fused and the detailed information of the image is recovered by the reconstruction module, named RM. Finally, multiple loss functions are linearly superimposed, and the adversarial network is trained iteratively to obtain the enhanced underwater images. The algorithm is designed to accommodate various underwater scenes, providing both color correction and detail enhancement. We conducted a comprehensive evaluation of the proposed algorithm, considering both qualitative and quantitative aspects. The experimental results demonstrate the effectiveness of our approach on a diverse underwater image dataset. The proposed algorithm exhibits superior performance in terms of enhancing underwater image quality, achieving significant improvements in contrast, color accuracy, and detail preservation. The proposed methodology exhibits promising results, offering potential applications in various domains such as underwater photography, marine exploration, and underwater surveillance.