Underwater image color correction via global and local two-step optimization

Underwater images usually suffer from quality degradation due to light absorption and scattering, leading to color distortion, blurred details, and low contrast. To address these challenges, we propose a global and local two-step optimization method (GLTO). Specifically, we first analyze the statistical features of natural images in the CIELab color space. Meanwhile, we design a heuristic global optimization strategy that minimizes the feature differences between underwater and natural images to restore the color and luminance of the raw image. We develop a local optimization strategy based on luminance information, which uses guided filtering to decompose the luminance channel into large-scale and small-scale high-frequency images and weighted fusion of them to obtain a detail-enhanced luminance channel. Finally, we leverage the local illumination intensity of the image captured by the luminance channel to adjust the local color distortion. Extensive experimental evaluations have demonstrated the superiority of our proposed GLTO method in underwater image preprocessing, which substantially enhances the performance of subsequent image enhancement. The project can be found at https://github.com/yubaiqiang/GLTO.