ITW-DehazeFormer: Imaging through Turbid Water Using Improved DehazeFormer
Qicong Wang, Xiaopin Zhong, Dajiang Lu, Yibin Tian
Abstract
Light scattering and absorption degrade the quality of underwater images, and various image enhancement methods have been explored. However, the existing underwater image datasets lack corresponding high-quality references, and the degree of scattering and absorption is not strictly controlled. In this study, we constructed an image dataset with different degrees of light scattering and controlled water turbidity via a water tank. The Swin Transformer based dehazing network DehazeFormer has been improved, termed ITW-DehazeFormer, to enhance images acquired through turbid water. First, a histogram equalization pre-enhancement block is added. Second, the SKfusion block is replaced by a content-guided attention based fusion block to combine channel and spatial attention so that information interactions between different channels are guaranteed. Finally, a hybrid loss function combining space and frequency domain information is introduced. Experimental results show that ITW-DehazeFormer outperforms seven existing image enhancement methods in terms of several image quality metrics, including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and multi-scale SSIM.
BibTeX
@inproceedings{icassp2025_itwdehazeformeri,
title = {ITW-DehazeFormer: Imaging through Turbid Water Using Improved DehazeFormer},
author = {Qicong Wang and Xiaopin Zhong and Dajiang Lu and Yibin Tian},
booktitle = {ICASSP 2025},
year = {2025}
}