UIDEF: A real-world underwater image dataset and a color-contrast complementary image enhancement framework

Numerous underwater image enhancement methods have been proposed in recent years. However, these methods are mainly evaluated using synthetic datasets with similar degradation or real-world datasets with insufficient images. A benchmark dataset containing various degraded situations and real-world underwater images is needed to evaluate the performance of these methods. In this paper, we propose a Real-world Underwater Image Dataset (UIDEF), which consists of seven categories and eleven subcategories with 9200 real-world underwater images. These categories roughly cover the multiple degradation types and different shooting perspectives of common underwater imagery. Using this dataset, we conduct a qualitative and quantitative empirical comparison of eight state-of-the-art underwater image enhancement methods to evaluate their effectiveness and robustness. Considering that these methods cannot handle both color restoration and contrast enhancement of the underwater degraded images well, we present a color-contrast complementary image enhancement framework that consists of the adaptive color perception balance and multi-scale weighted fusion. The former procedure is essential to remove the color cast of original input images, while the latter defines four attentive weight maps for making the enhanced output images present a more comfortable visual perception. Extensive experiments have validated that our framework achieves relatively satisfactory performance in most cases. In addition, we demonstrate an additional application of UIDEF in reconstructing a wider-field underwater image based on multiple images with overlapping regions. The dataset is available at https://github.com/LaibinChang/UIDEF.git.