A diverse underwater image formation model for underwater image restoration

The underwater images (UWIs) are one of the most effective sources to collect information about the underwater environment. Due to the irregular optical properties of different water types, the captured UWIs suffer from color cast, low visibility and distortion. Moreover, each water type offers different optical absorption, scattering, and attenuation of red, green and blue bands, which makes restoration of UWIs a challenging task. The revised underwater image formation model (RUIFM) considers only the peak values of the corresponding attenuation coefficient of each water type to restore UWIs. The performance of RUIFM suffers due to the inter-class variations of UWIs in a water type. In this paper, we propose an improved version of RUIFM as the Diverse Underwater Image Formation Model (DUIFM). The DUIFM increases the diversity of RUIFM by deeply encountering the optical properties of each water type. We investigate the inter-class variations of Jerlov-based classes of UWIs in terms of light attenuation and statistical features and further classify each image into low, medium and high bands. Which, in turn, provides the precise inherent optical attenuation coefficient of water and increases the generality of the DUIFM in color restoration and enhancement. The qualitative and quantitative performance evaluation results on publicly available real-world underwater enhancement (RUIE), underwater image enhancement benchmark (UIEB) and enhanced underwater visual perception (EUVP) data sets demonstrate the effectiveness of our proposed DUIFM.