IGFU: A Hybrid Underwater Image Enhancement Approach Combining Adaptive GWA, FFA-Net With USM

Abstract

To address the issue of color distortion and blurriness in underwater imageries, a hybrid Underwater Image Enhancement (UIE) method combining Adaptive Gray World Algorithm (GWA), Feature Fusion Attention Network (FFA-Net) and Unsharp Masking (USM) is proposed in this research. This method begins with color correction by applying different stretching processes to the RGB components based on the image's color information, and iteratively corrects the colors. Next, the corrected image undergoes dehazing via FFA-Net to eliminate underwater haze and improve clarity. Ultimately, USM is applied to amplify high-frequency components, thus enhancing edge details. Qualitative and quantitative comparisons demonstrate that the proposed Improved GWA FFA-Net USM (IGFU) method outperforms existing techniques in underwater image quality.