Underwater Image Enhancement Frame Based on Color Balance and Feature Fusion

Abstract

Owing to the complexity of underwater environments and the scattering and absorption of light, underwater images often suffer from issues such as image blurring, color distortion, and low visibility. To improve image quality, an image enhancement frame based on color balance and feature fusion is proposed. First, the Dark Channel Prior (DCP) parameters are optimized using a method that combines a quadtree search with light attenuation characteristics to address the issue of image blurring. Second, a differential compensation is applied to the two attenuated channels used for the deblurred image for achieving a color balanced image. Subsequently, to address the issues of detail loss and low contrast in color balanced images, guided filtering is employed to decompose the image and a nonlinear stretching function is introduced for improving the detail layer, resulting in a detail enhanced image. Using Contrast Limited Adaptive Histogram Equalization (CLAHE), normalized gamma correction is added to obtain contrast enhanced images. Finally, weight maps containing different features are extracted from the detail and contrast enhanced images and a multiscale pyramid strategy is adopted for fusion to obtain the final enhanced image. The experimental results demonstrate that compared to the references, this method improves the average values of underwater image quality metrics, average gradient, and patch-based contrast quality index by 17.6%, 76.4%, and 11.2%, respectively. This method exhibits robust performance in improving image quality and can achieve various image enhancement effects.