snnTrans-DHZ: A Lightweight Spiking Neural Network Architecture for Underwater Image Dehazing
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| Format: | Preprint |
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2025
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| _version_ | 1866913795692036096 |
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| author | Sudevan, Vidya Zayer, Fakhreddine Kausar, Rizwana Javed, Sajid Karki, Hamad De Masi, Giulia Dias, Jorge |
| author_facet | Sudevan, Vidya Zayer, Fakhreddine Kausar, Rizwana Javed, Sajid Karki, Hamad De Masi, Giulia Dias, Jorge |
| contents | Underwater image dehazing is critical for vision-based marine operations because light scattering and absorption can severely reduce visibility. This paper introduces snnTrans-DHZ, a lightweight Spiking Neural Network (SNN) specifically designed for underwater dehazing. By leveraging the temporal dynamics of SNNs, snnTrans-DHZ efficiently processes time-dependent raw image sequences while maintaining low power consumption. Static underwater images are first converted into time-dependent sequences by repeatedly inputting the same image over user-defined timesteps. These RGB sequences are then transformed into LAB color space representations and processed concurrently. The architecture features three key modules: (i) a K estimator that extracts features from multiple color space representations; (ii) a Background Light Estimator that jointly infers the background light component from the RGB-LAB images; and (iii) a soft image reconstruction module that produces haze-free, visibility-enhanced outputs. The snnTrans-DHZ model is directly trained using a surrogate gradient-based backpropagation through time (BPTT) strategy alongside a novel combined loss function. Evaluated on the UIEB benchmark, snnTrans-DHZ achieves a PSNR of 21.68 dB and an SSIM of 0.8795, and on the EUVP dataset, it yields a PSNR of 23.46 dB and an SSIM of 0.8439. With only 0.5670 million network parameters, and requiring just 7.42 GSOPs and 0.0151 J of energy, the algorithm significantly outperforms existing state-of-the-art methods in terms of efficiency. These features make snnTrans-DHZ highly suitable for deployment in underwater robotics, marine exploration, and environmental monitoring. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_11482 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | snnTrans-DHZ: A Lightweight Spiking Neural Network Architecture for Underwater Image Dehazing Sudevan, Vidya Zayer, Fakhreddine Kausar, Rizwana Javed, Sajid Karki, Hamad De Masi, Giulia Dias, Jorge Computer Vision and Pattern Recognition Artificial Intelligence Performance Robotics Image and Video Processing Underwater image dehazing is critical for vision-based marine operations because light scattering and absorption can severely reduce visibility. This paper introduces snnTrans-DHZ, a lightweight Spiking Neural Network (SNN) specifically designed for underwater dehazing. By leveraging the temporal dynamics of SNNs, snnTrans-DHZ efficiently processes time-dependent raw image sequences while maintaining low power consumption. Static underwater images are first converted into time-dependent sequences by repeatedly inputting the same image over user-defined timesteps. These RGB sequences are then transformed into LAB color space representations and processed concurrently. The architecture features three key modules: (i) a K estimator that extracts features from multiple color space representations; (ii) a Background Light Estimator that jointly infers the background light component from the RGB-LAB images; and (iii) a soft image reconstruction module that produces haze-free, visibility-enhanced outputs. The snnTrans-DHZ model is directly trained using a surrogate gradient-based backpropagation through time (BPTT) strategy alongside a novel combined loss function. Evaluated on the UIEB benchmark, snnTrans-DHZ achieves a PSNR of 21.68 dB and an SSIM of 0.8795, and on the EUVP dataset, it yields a PSNR of 23.46 dB and an SSIM of 0.8439. With only 0.5670 million network parameters, and requiring just 7.42 GSOPs and 0.0151 J of energy, the algorithm significantly outperforms existing state-of-the-art methods in terms of efficiency. These features make snnTrans-DHZ highly suitable for deployment in underwater robotics, marine exploration, and environmental monitoring. |
| title | snnTrans-DHZ: A Lightweight Spiking Neural Network Architecture for Underwater Image Dehazing |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Performance Robotics Image and Video Processing |
| url | https://arxiv.org/abs/2504.11482 |