Image dehazing based on Uformer modified WGAN

Qixiang Wang; Yannan Yang; Wende Dong

doi:10.1117/12.3016206

30 April 2024 Image dehazing based on Uformer modified WGAN

Qixiang Wang, Yannan Yang, Wende Dong

Proceedings Volume 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition; 131560P (2024) https://doi.org/10.1117/12.3016206
Event: Sixth Conference on Frontiers in Optical Imaging Technology and Applications (FOI2023), 2023, Nanjing, JS, China

Abstract

In hazy weather, the obtained images of optical instruments are severely degraded due to the multiple atmospheric light scattering, which will significantly influence subsequent image processing such as target recognition and location. In this paper, we propose an efficient image dehazing network based on the framework of the Wasserstein generative adversarial network (WGAN). Inspired by the classic U-net network architecture, we first use a transformer-based image restoration architecture Uformer to modify the generator of WGAN. Then for loss function design, according to the requirement of the image dehazing task, the overall network training is constrained from two aspects, i.e., the pixel loss and the adversarial loss. Finally, the synthetic haze dataset was used to train and evaluate the effectiveness of the network. The results show that the proposed method can obtain high quality restored images, which is comparable to some current methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qixiang Wang, Yannan Yang, and Wende Dong "Image dehazing based on Uformer modified WGAN", Proc. SPIE 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition, 131560P (30 April 2024); https://doi.org/10.1117/12.3016206

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