RETINEX UNDERWATER IMAGE ENHANCEMENT WITH MULTIORDER GRADIENT PRIORS

We develop a variational retinex algorithm for enhancing single underwater image with multiorder gradient priors of reflectance and illumination. First, a simple yet effective color correction approach is used to remove color casts and recover naturalness. Then, a variational retinex model for enhancing the color-corrected underwater image is established by imposing multiorder gradient priors of reflectance and illumination. According to structural sparsity difference between illumination and reflectance, the l(1) norm is accurately adopted to model piecewise and piecewise linear approximations on the reflectance, while the l(2) norm is appropriately employed to enforce spatial smoothness and spatial linear smoothness on the illumination. Next, a complex underwater image enhancement issue is turned into simple denoising subproblems, which can be addressed by an efficient optimization algorithm that is fast performed on pixel-wise operations without requiring additional prior knowledge about underwater imaging conditions. Final experiments demonstrate that the proposed method yields better results of qualitative and quantitative assessments than several traditional and leading underwater image enhancement approaches.

Automated summary

The algorithm enhances single underwater image using variational Retinex model with multiorder gradient priors of reflectance and illumination. By separating the underwater image enhancement issue into denoising subproblems based on prior knowledge, a better result is achieved compared to traditional methods.