Underwater Scene Clarity Reconstruction via Multilayer Information Fusion and Self-Organized Stitching

Weibo Zhang, Hao Wang, Peng Ren, Weidong Zhang

Abstract: Single underwater image often suffer from severe quality degradation and field-of-view limitation due to the underwater light propagation characteristics and the viewing range of camera equipment. To address these challenges, we propose a underwater scene clarity reconstruction framework called USCR, which comprises a multilayer information fusion (MIF) method for underwater image enhancement (UIE) and a self-organized stitching (SOS) method for image stitching. First, MIF corrects color distortion, enhances contrast, and highlights image detail information through a minimally attenuated channel guided color correction strategy and a gradient weight fusion strategy. Subsequently, SOS is applied to stitch the enhanced underwater images, which utilizes a homography matrix to initially stitch the image sequence, and further employs a pixel blending strategy based on boundary distance weighting for boundary pixel fusion to the initial stitch image, aiming to ensure a homogeneous transition of the stitch region. Our reconstructed underwater scenes are characterized by visual clarity and a wide field-of-view. Extensive qualitative and quantitative experimental validations show that USCR outperforms the state-of-the-art methods in underwater visual reconstruction task.

External IDs:doi:10.1109/tcsvt.2025.3608828