U-Net-Like Spiking Neural Networks for Single Image Dehazing
This work addresses image clarity enhancement in hazy conditions for computer vision applications, representing an incremental improvement by combining existing U-Net and SNN concepts with a novel block.
The paper tackles single image dehazing by proposing DehazeSNN, a U-Net-like spiking neural network that integrates an Orthogonal Leaky-Integrate-and-Fire Block, achieving competitive state-of-the-art performance with reduced computational burden and smaller model size.
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.