Dual-model: Revised imaging network and visual perception correction for underwater image enhancement

Abstract

Considering the limitation of a single physics-based or deep learning-based method, this paper proposes an effective Dual-model methodology for underwater image enhancement. The Dual-model mainly consists of two branches: the Revised Imaging Network-model (RIN-model) and the Visual Perception Correction-model (VPC-model). Specifically, instead of directly learning an image-to-image mapping strategy, the RIN-model constructs a compact network to estimate the ambient-light and direct-transmission parameters, which are used to reconstruct the preliminary enhanced image based on the revised underwater image formation model. Based on the pre-trained RIN-model, the ambient-light and direct-transmission parameters estimated using only a single input image may be biased, especially for these severely degraded underwater images. To further correct the visual perception of the preliminary enhanced image, the VPC-model incorporates the adaptive-stretching histogram and relative-stretching histogram for contrast enhancement and color restoration, respectively. We also present an adaptive color balance compensation method to estimate the maximum attenuation channel and equalize the information distribution of the three channels in the preliminary enhanced image, which acts as a transition procedure to prevent color distortion or detail destruction caused by excessive stretching in the VPC-model. Comprehensive qualitative and quantitative evaluations have revealed that our Dual-model outperforms seven state-of-the-art underwater image enhancement methods, including the MLLE, Haze-Lines, HLRP, ACDC, UWCNN, FUnIE-GAN, and Water-Net. In the UIEB and EUVP datasets, our methodology achieves relatively better performance than other methods on both full-reference and no-reference evaluation metrics. We also validate that the proposed methodology facilitates the advanced image applications of local feature matching and edge detection.