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Image Dehazing Using a Convolutional Autoencoder Network with Integrated Convolutional Block Attention

Document Type : Applied Article

Authors

RIV Lab., Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran

Abstract

One of the challenges in digital image processing that we face today is the presence of haze in images. This challenge is particularly prominent in imaging areas with humid and rainy weather compared to other locations. Examples of AI-based systems that can be impacted by this type of challenge include smart traffic control cameras, autonomous vehicles, and Video Assistant Referee (VAR) systems in football stadiums, security and surveillance cameras, and more. Therefore, this paper aims to propose a method that can mitigate this problem using Self-Supervised Learning (SSL) and deep learning. To this end, a Convolutional Autoencoder Network (CAN) with Convolutional Block Attention Module (CBAM) was proposed to reduce haze from images. The advantage of the proposed method is using fewer layers and filters compared to other models introduced by previous researchers in this field and using more important convolutional channels and important image regions using CBAM. Experiments in this paper reveal that overusing large or numerous convolutional filters to generate diverse features can reduce a model's ability to dehaze images effectively. Thus, the number of filters should be carefully limited. On the other hand, a combined loss function was used to train the proposed architecture. The proposed model was trained and tested using NH-haze dataset and the Realistic Single Image Dehazing (RESIDE) dataset. To evaluate our method, we used structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). The test results of the proposed architecture showed that it has higher performance compared to the state-of-the-art in the field.

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References
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Journal of AI and Data Mining
Volume 13, Issue 4
October 2025
Pages 393-405
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