Underwater image restoration method based on Walsh–Hadamard transform and attenuation coefficient estimation

Abstract Underwater images often exhibit color distortion and low contrast due to the scattering and absorption of light as it travels through water. Changes in lighting conditions further complicate the restoration and enhancement of these images. Improving the quality of underwater images is crucial for advancements in fields such as marine biology research, underwater measurement, and environmental monitoring. This paper proposes an underwater image restoration method based on the Image Formation Model (IFM), utilizing the Walsh–Hadamard transform and attenuation coefficient estimation. Traditional methods rely on dark channel prior and maximum intensity prior to estimate background light (BL) and transmission maps (TMs), often performing poorly in various underwater environments. Our method uses image blur to estimate BL and depth maps and derives three-channel attenuation coefficients using the gray-world theory to obtain a more accurate TM. Experimental results on real underwater images show that our method effectively eliminates color deviation and contrast distortion while preserving image details, significantly outperforming other IFM-based restoration techniques. Compared to the closest competing algorithms, our method achieves better UIQM and UCIQE scores.