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AGD-Net: Attention-Guided Dense Inception U-Net for Single-Image Dehazing

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Abstract

Image hazing poses a significant challenge in various computer vision applications, degrading the visual quality and reducing the perceptual clarity of captured scenes. The proposed AGD-Net utilizes a U-Net style architecture with an Attention-Guided Dense Inception encoder-decoder framework. Unlike existing methods that heavily rely on synthetic datasets which are based on CARLA simulation, our model is trained and evaluated exclusively on realistic data, enabling its effectiveness and reliability in practical scenarios. The key innovation of AGD-Net lies in its attention-guided mechanism, which empowers the network to focus on crucial information within hazy images and effectively suppress artifacts during the dehazing process. The dense inception modules further advance the representation capabilities of the model, facilitating the extraction of intricate features from the input images. To assess the performance of AGD-Net, a detailed experimental analysis is conducted on four benchmark haze datasets. The results show that AGD-Net significantly outperforms the state-of-the-art methods in terms of PSNR and SSIM. Moreover, a visual comparison of the dehazing results further validates the superior performance gains achieved by AGD-Net over other methods. By leveraging realistic data exclusively, AGD-Net overcomes the limitations associated with synthetic datasets which are based on CARLA simulation, ensuring its adaptability and effectiveness in real-world circumstances. The proposed AGD-Net offers a robust and reliable solution for single-image dehazing, presenting a significant advancement over existing methods.

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Authors and Affiliations

  1. Department of Electrical & Electronics Engineering, BITS-Pilani, Pilani Campus, Pilani, 333031, India

    Amit Chougule & Agneya Bhardwaj

  2. Department of Electrical and Electronics Engineering & APPCAIR, BITS-Pilani, Pilani Campus, Pilani, 333031, India

    Vinay Chamola

  3. Department of Computer Science Information Systems, BITS-Pilani, Pilani Campus, Pilani, 333031, India

    Pratik Narang

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  1. Amit Chougule
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Correspondence to Vinay Chamola.

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Chougule, A., Bhardwaj, A., Chamola, V. et al. AGD-Net: Attention-Guided Dense Inception U-Net for Single-Image Dehazing. Cogn Comput 16, 788–801 (2024). https://doi.org/10.1007/s12559-023-10244-2

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