Turbid Underwater Image Enhancement With Illumination-Constrained and Structure-Preserved Retinex Model
Shuai Liu, Yuchao Zheng, Jianru Li, Huimin Lu, Dong An, Zhengxiang Shen, Zhanshan Wang · IEEE Transactions on Circuits and Systems for Video Technology · 2025
Turbid underwater images often suffer from color distortion, contrast degradation, and detail loss. To improve the visual quality of these images, this paper proposes an illumination-constrained, structure-preserved retinex variational model. The proposed approach consists of three main components: a nonlinear model based on the classical retinex theory to represent the multiple adverse deformations of turbid underwater images; an adaptive channel compensation method to correct the color cast; and an illumination-constrained structure-preserved variational retinex model that simultaneously estimates a smooth illumination component and a detail display reflection component and uniformly predicts the noise pattern of preprocessed underwater images. Specifically, an adaptive weight matrix is proposed to reveal the structural details in reflectance. The overall smoothness of illumination is constrain by exponential guided filtering and l1/2 norm. The total intensity of the noise pattern is constrained by l2 norm. To solve the resulting optimization problem, we employ alternating direction minimization of logless transformations of Lagrange multipliers. Extensive experiments demonstrate the effectiveness of the proposed method in improving the quality of turbid underwater images. Beyond subjective visual observations, the method also exhibits competitive performance in objective image quality evaluations.