U-Net-Like SNN for Single Image Dehazing
Published:Dec 30, 2025 02:38
•1 min read
•ArXiv
Analysis
This paper introduces DehazeSNN, a novel architecture combining a U-Net-like design with Spiking Neural Networks (SNNs) for single image dehazing. It addresses limitations of CNNs and Transformers by efficiently managing both local and long-range dependencies. The use of Orthogonal Leaky-Integrate-and-Fire Blocks (OLIFBlocks) further enhances performance. The paper claims competitive results with reduced computational cost and model size compared to state-of-the-art methods.
Key Takeaways
- •Proposes DehazeSNN, a novel architecture for single image dehazing.
- •Combines U-Net-like design with Spiking Neural Networks (SNNs).
- •Utilizes Orthogonal Leaky-Integrate-and-Fire Blocks (OLIFBlocks) for enhanced performance.
- •Achieves competitive results with reduced computational cost and model size.
- •Publicly available code at https://github.com/HaoranLiu507/DehazeSNN.
Reference
“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.”