Polarization image defogging based on detail recovery generative adversarial network

Cheng Yang; Yang Li

doi:10.1117/12.3004738

20 October 2023 Polarization image defogging based on detail recovery generative adversarial network

Cheng Yang, Yang Li

Proceedings Volume 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023); 129160F (2023) https://doi.org/10.1117/12.3004738
Event: Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 2023, Kunming, China

Abstract

Most of the existing polarization image based defogging methods use a priori and assumptions to recover images, and although these methods have made great progress, the a priori or assumptions are not always reliable in practical scenarios, which limits the performance of defogging methods. In this paper, we design a two branch network to learn image features; the defogging network uses the fusion module to adaptively assign weights to different path features to ensure the defogging effect while focusing on the haze region; the detail recovery network uses convolutional layers and smoothly expanding convolution to expand the perceptual field and fully obtain local and global feature information; finally, the features obtained from the detail recovery network and the defogging network are fused to improve the defogging Finally, the features obtained from detail recovery network and defogging network are fused to improve the defogging effect. The experimental results show that this method can reconstruct clear images in foggy environment with 2db improvement in PSNR and 0.016 improvement in SSIM, and the quality of reconstructed images is better than existing algorithms.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Cheng Yang and Yang Li "Polarization image defogging based on detail recovery generative adversarial network", Proc. SPIE 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 129160F (20 October 2023); https://doi.org/10.1117/12.3004738

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KEYWORDS

Image restoration

Polarization

Image fusion

Feature fusion

Feature extraction

Convolution

Fiber optic gyroscopes

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