Meta underwater camera: A smart protocol for underwater image enhancement

Abstract

Underwater images captured by an underwater camera normally suffer from visual degradation issues, such as color deviations, low contrasts, and detail blurs. Existing studies tend to address these issues separately by individual techniques, such that underwater image visibility can hardly be improved in an overall, consistent manner. To address this limitation, we present a smart protocol for underwater image enhancement. The protocol comprises a comprehensive cascade of seven image enhancement techniques, i.e., attenuated channel compensation, white balance, tone mapping, hue-saturation-lightness (HSL) model-based saturation adjustment, contrast stretching, gamma correction, and high-pass fusion. The protocol is smartly configured by reinforcement learning. Specifically, a set of underwater image multi-color space features are considered as a state, a set of parameter values for the protocol as an action, and an increment of a non-reference visual preference score as a reward. By the reinforcement learning strategy, the parameter values for the seven techniques in the protocol are optimally configured as a whole, resulting in optimal underwater image enhancement results. We refer to the functionality of the smart protocol as a meta underwater camera (MUC) methodology because it operates behind an underwater camera’s operation of capturing underwater images but provides underwater images of high visibility beyond those of low visibility originally obtained from the underwater camera. Qualitative and quantitative empirical results validate that our MUC methodology outperforms state-of-the-art underwater image enhancement methods. We release the reproducible code at https://gitee.com/wanghaoupc/MUC_UnderwaterImageEnhancement for public evaluations.