Processes, Vol. 13, Pages 2628: Overwater-Haze: A Large-Scale Overwater Paired Image Dehazing Dataset

Processes doi: 10.3390/pr13082628

Authors:
Yuhang Xie
Meng Li
Siqi Wang
Hongbo Wang

Maritime navigation safety relies on high-precision perception systems. However, hazy weather often significantly compromises system performance, particularly by reducing image quality and increasing navigational risks. Although image dehazing techniques provide an effective solution, the lack of dedicated overwater dehazing datasets limits the generalization of dehazing algorithms. To overcome this problem, we present a large-scale overwater paired image dehazing dataset: Overwater-Haze. The dataset contains 21,000 synthetic overwater hazy images generated based on the atmospheric scattering model (ASM), categorized into Mist, Moderate, and Dense subsets based on varying haze concentrations, and 500 real overwater hazy images, which form the Real-Test portion of the test set. In order to meet the requirements for background interference mitigation, image diversity, and high quality, we performed extensive data augmentation and developed a comprehensive dataset creation pipeline. Our evaluation of five dehazing algorithms shows that models trained on Overwater-Haze achieve 9.96% and 10.47% lower Natural Image Quality Evaluator (NIQE) and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) scores than pre-trained models on real overwater scenes, demonstrating the value of Overwater-Haze in assessing algorithm performance in overwater environments.

Source link

Yuhang Xie www.mdpi.com