Abstract: Underwater optical images are the most direct and efficient way to obtain underwater information， but they often suffer from severe color distortion and contrast degradation due to light absorption and scattering. Prevailing deep learning methods require extensive paired data and have limitations such as complex network architectures and high computational costs. In this paper，a hyperbolic imaging correction model for underwater image enhancement based on zero-reference learning framework is proposed. It transforms image enhancement problem into a specific parametric graph estimation task. In the color enhancement stage，a physically grounded color correction model derived from haze imaging principle is developed. A lightweight network is designed to estimate dynamic adjustment parameters and to eliminate dispersion and color cast，while optimizing pixel-level dynamic range. In the luminance optimization stage，a non-linear luminance mapping function coupled with an adaptive brightness correction model is deployed，which is inspired by the human visual system. Thus，residual dynamic range compression and illumination constraints from prior processing are rectified，global luminance is calibrated and model bias is corrected. The core contribution of this model lies in the zero-reference learning feature，and the framework operates without paired underwater data. Experiments on benchmark datasets show that the proposed method is competitive in both subjective and quantitative metrics，validating the methodological efficacy and practical viability of this physics-inspired correction framework for underwater vision.