An Efficient Accelerator for Dehazing Neural Network
An Efficient Accelerator for Dehazing Neural Network Based on Physical Perception Model and Cross-Scale Pixel Attention
Abstract:
Haze reduces visibility, hindering real-time image processing applications. Although deep learning-based dehazing algorithms can significantly enhance image quality, their high computational complexity and storage demands make them challenging to deploy on resource-constrained hardware plat-forms. To address this, we propose an efficient dehazing neural network featuring physical perception model and cross-scale pixel attention (EP-CSANet), together with its hardware accelerator. In terms of algorithm, we design an efficient physical percep-tion model (EPM) based on the atmospheric scattering model (ASM), combined with adaptive channel attention (ACA), which accurately approximates atmospheric light (A) and transmittance [t(x)], and effectively adapts to various environmental condi-tions, significantly improving dehazing accuracy. Furthermore, to enhance multiscale information interaction and preserve fine-grained texture details, we introduce cross-scale pixel attention (CSPA), which utilizes a dual-branch approach to extract high receptive-field information while maintaining fine-grained tex-tures. In terms of hardware, we design a dedicated FPGA-based dehazing acceleration architecture. Through an efficient 16-stage pipeline, we achieve a dehazing rate of 127 frames per second (fps) for 640 × 480 images, meeting real-time processing requirements. In addition, by incorporating sparse convolution optimization techniques, we significantly improve resource uti-lization: LUTs increased by 31.8%, FFs by 27.9%, and DSPs by 22.3%. Experimental results demonstrate that EP-CSANet, using only 2361 parameters on the SOTS dataset, outperforms other dehazing algorithms. The source code is publicly available at https://github.com/netflymachine/EP-CSANet
” Thanks for Visit this project Pages – Register This Project and Buy soon with Novelty “
An Efficient Accelerator for Dehazing Neural Network Based on Physical Perception Model and Cross-Scale Pixel Attention
