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LCDA-Net: Efficient Image Dehazing with Contrast-Regularized and Dilated Attention

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Abstract

To address the issues of incomplete dehazing and low dehazing efficiency in existing dehazing networks, this study introduces a Lightweight Contrast-Regularized Dilated Attention Network (LCDA-Net) for single-image dehazing. Initially, Attention Context Encoding (ACE) is employed to decompose the input image into high-frequency and low-frequency features. For the low-frequency features, which are significantly impacted by haze, a pyramid dehazing module based on large-kernel dilated convolutional attention is devised, facilitating efficient dehazing through complementary semantic information. In contrast, for high-frequency features, a detail enhancement module based on deformable convolution is designed to restore fine texture information. Subsequently, high-frequency and low-frequency features are merged to reconstruct a clear image. Lastly, a loss function is designed by incorporating contrast regularization and edge loss strategies, effectively guiding the network to generate more realistic images. In this network, depthwise separable convolutions replace traditional convolutions, significantly reducing model complexity while maintaining satisfactory dehazing performance. Experimental results on the RESIDE benchmark dataset demonstrate that, compared to other advanced methods, the proposed approach achieves superior dehazing outcomes for both synthetic and real haze images, effectively mitigating artifacts, distortions, and incomplete dehazing. The PSNR on the SOTS indoor and outdoor test sets reaches 31.73 dB and 29.31 dB, respectively, with a network parameter size of merely 2 M. Additionally, the proposed method exhibits the lowest model complexity while achieving optimal performance metrics and the highest FPS, indicating both its superior dehazing performance and low complexity.

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Acknowledgements

This work was Supported by National Natural Science Foundation of China (Grant No. 12071126).

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Authors and Affiliations

  1. College of Information Science and Engineering, Hunan Normal University, No. 36 Lushan Road, Changsha, 410081, Hunan, China

    Xun Luo, Shuo Cao, Zizheng Wang & Yiyang Chen

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  1. Xun Luo
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  2. Shuo Cao
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  3. Zizheng Wang
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  4. Yiyang Chen
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Contributions

XL and SC designed and carried out the experimental studies. SC was responsible for data analysis and interpretation. The main manuscript text and literature review were written by SC and ZW. All tables and figures were prepared by ZW and YC, who also participated in the design of the experiment. XL provided crucial equipment and research materials. All authors collectively reviewed and revised the manuscript and agreed to the final draft of the paper.

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Correspondence to Xun Luo.

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Luo, X., Cao, S., Wang, Z. et al. LCDA-Net: Efficient Image Dehazing with Contrast-Regularized and Dilated Attention. Neural Process Lett 55, 11467–11488 (2023). https://doi.org/10.1007/s11063-023-11384-0

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