Enhancing underwater image quality: a multilevel model with color correction and extended transmission map

Underwater image enhancement has attained significant attention due to its applications in different fields such as marine engineering and aquatic robotics. An underwater image suffers from various deterioration issues such as color loss, degraded contrast, and poor visibility due to the attenuation and scattering of light and color. In recent years, many underwater image techniques have been proposed. However, these techniques introduce some undesired color tones with heavily attenuated color channels in deep water images and also do not work well for different types of hazy underwater images. To tackle these issues, we propose a color correction network and an underwater extended dark channel method that handles color cast issues and adaptively controls the different levels of hazy underwater images. The proposed model offers not only high-quality enhanced underwater images but also preserves the surface details. The experimental results are evaluated both qualitatively and quantitatively on three underwater benchmark datasets, namely, underwater image enhancement benchmark, underwater color cast removal and color correction, and enhancing underwater visual perception, and it is found that the proposed method outperforms the state-of-the-art approaches. In addition, the proposed method is generalized to remove the different underwater image degradation issues such as haze, low light, and color degradation. The code for the paper is available at https://github.com/sangeeta-rani/underwater-image-enhacement-using-extented-transmission-map.