Underwater Images Enhancement by Revised Underwater Images Formation Model - Soo-Chang Pei; Chia-Yi Chen (IEEE Access, 2022)
In this paper, we proposed the efficient and appealing technique for underwater images enhancement. Underwater images often suffer from haze, color distortion, low contrast and loss of the human acuity due to light scattering and absorption. To tackle these issues, proposed precise model is presented expressively showed that: (1) the estimation of the transmission map in atmospheric scatter model is divided into two cases, which are more applicable to underwater images in different situations, and make the enhanced results more robust; (2) model not only removes haze but also restores lost colors of underwater images in the image de-hazing step instead of color correction. First, we proposed a revised underwater dehazing model aiming to eliminate the color of water directly while solving the problem of haze in the underwater images. Then proposed color correction method can adaptively address the problem of color shifting without any additional information. Furthermore, we design a multi-scale illumination fusion to reveal more details and low illumination parts of the image. Experimental results demonstrate that our proposed method outperforms other methods significantly with $5% \sim 77%$ quantitative improvement on all four evaluation performance indices and shows more obvious detailed underwater images. Our method can be applied to underwater detection and exploration as the pre-processing step.