A natural-based fusion strategy for underwater image enhancement

Underwater images generally are characterized by color cast and low contrast due to selective absorption and light scattering in water medium. Such degraded images reveal some limitations when used for further analysis. To overcome underwater image degradation, various enhancement techniques are developed. Especially, the fusion-based methods have made remarkable success in this filed. However, there are still some defects in the fusion of input images and weight maps, which cause their results to be unnatural. In this paper, we propose a novel and effective natural-based fusion method for underwater image enhancement that applies several image processing algorithms. First, we design an adaptive underwater image white balance method motivated by our statistical prior to mitigate the impact of color deviation of underwater scenes. We then derive two inputs that represent local detail-improved and global contrast-enhanced versions of the color corrected image. Instead of explicitly estimating weight map, like most existing algorithms, we propose a naturalness-preserving weight map estimation (NP-WME) method, which models the weight map estimation as an optimization problem. Particle swarm optimization (PSO) is used to solve it. Benefiting a proper weighting, the proposed method can achieve a trade-off between detail enhancement and contrast improvement, resulting a natural appearance of the fused image. Through this synthesis, we merge the advantages of different algorithms to obtain the output image. Experimental results show that the proposed method outperforms the several related methods based on quantitative and qualitative evaluations.