U-Net-Like Spiking Neural Networks for Single Image Dehazing
Image dehazing is a critical challenge in computer vision, essential for enhancing image clarity in hazy conditions. Traditional methods often rely on atmospheric scattering models, while recent deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Transformers, have improved performance by effectively analyzing image features. However, CNNs struggle with long-range dependencies, and Transformers demand significant computational resources. To address these limitations, we propose DehazeSNN, an innovative architecture that integrates a U-Net-like design with Spiking Neural Networks (SNNs). DehazeSNN captures multi-scale image features while efficiently managing local and long-range dependencies. The introduction of the Orthogonal Leaky-Integrate-and-Fire Block (OLIFBlock) enhances cross-channel communication, resulting in superior dehazing performance with reduced computational burden. Our extensive experiments show that DehazeSNN is highly competitive to state-of-the-art methods on benchmark datasets, delivering high-quality haze-free images with a smaller model size and less multiply-accumulate operations. The proposed dehazing method is publicly available at https://github.com/HaoranLiu507/DehazeSNN.
69565f6d832867f2535257dd
Huibin Li
69565f6d832867f2535257de
Haoran Liu
claimed_verified
2026-01-02T15:38:37.495Z
67dfd511eb33688e48f7e69d
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Oluc8nC6H8h-56r39vXx0.png
Haoran Liu
FengShaner
user
FengShaner
69565f6d832867f2535257df
Mingzhe Liu
69565f6d832867f2535257e0
Yulong Xiao
69565f6d832867f2535257e1
Peng Li
69565f6d832867f2535257e2
Guibin Zan
U-Net-Like Spiking Neural Networks for Single Image Dehazing
Image dehazing is a critical challenge in computer vision, essential for enhancing image clarity in hazy conditions. Traditional methods often rely on atmospheric scattering models, while recent deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Transformers, have improved performance by effectively analyzing image features. However, CNNs struggle with long-range dependencies, and Transformers demand significant computational resources. To address these limitations, we propose DehazeSNN, an innovative architecture that integrates a U-Net-like design with Spiking Neural Networks (SNNs). DehazeSNN captures multi-scale image features while efficiently managing local and long-range dependencies. The introduction of the Orthogonal Leaky-Integrate-and-Fire Block (OLIFBlock) enhances cross-channel communication, resulting in superior dehazing performance with reduced computational burden. Our extensive experiments show that DehazeSNN is highly competitive to state-of-the-art methods on benchmark datasets, delivering high-quality haze-free images with a smaller model size and less multiply-accumulate operations. The proposed dehazing method is publicly available at https://github.com/HaoranLiu507/DehazeSNN.
DehazeSNN combines a U-Net-like architecture with Spiking Neural Networks and an Orthogonal Leaky-Integrate-and-Fire Block to achieve efficient and effective image dehazing with reduced computational resources.
Convolutional Neural Networks (CNNs) Transformers Spiking Neural Networks (SNNs) U-Net-like design Spiking Neural Networks (SNNs) Orthogonal Leaky-Integrate-and-Fire Block (OLIFBlock) cross-channel communication multiply-accumulate operations