中北大学学报（自然科学版）2025，Vol.46Issue(6)：726-733,8.DOI:10.62756/jnuc.issn.1673-3193.2025.01.0015
基于物理先验引导的记忆增强视频去雾算法
Memory Enhancement Video Dehazing Algorithm Based on Physics-Prior Guidance
摘要
Abstract
To address the limitations of existing deep learning-based video dehazing algorithms in effec-tively learning consistent prior features from long video sequences,which leads to residual haze and poor continuity in restored videos,this paper proposed a physics-prior guided memory-enhanced video dehazing algorithm.Firstly,a physics prior memory preservation module was designed.This module employed dense connections and residual structures to preserve spatial salient information,fused multi-scale features to enhance the modeling of physical haze priors,and encoded the enhanced priors into long-term memory.Secondly,to overcome the limitations of traditional memory mechanisms,we innovatively reformed the memory storage approach by decoupling the prior feature memory into key memory features and value memory features.Additionally,we designed a memory-enhanced physics-prior guidance module.This module dynamically guided the attention mechanism to extract correlated features from the value memory by calculating the similarity between the current frame's key prior features and the key memory matrix,thereby enhancing the prior features of the current frame.This process generated prior features with better spatiotemporal consistency and stronger global reasoning capabilities.Finally,the proposed framework,termed Multi-Range Temporal Alignment Network with Physical Prior(MAP-Net),integrated the phys-ics prior memory preservation module and the memory-enhanced physics prior guidance module.The experimental results show that compared with the suboptimal algorithm,the peak signal-to-noise ratio(PSNR)of the proposed algorithm improves by 0.71 dB and 0.14 dB,and the structural similarity(SSIM)improves by 0.006 1 and 0.006 7 on HazeWorld and REVIDE datasets,respectively.The pro-posed method not only eliminates the color distortion and the residual haze effectively to improve the visual realism,but also achieves 11 frame/s real-time processing on the NVIDIA Tesla P100 GPU.关键词
视频去雾/物理先验/记忆增强/图像处理/神经网络/深度学习Key words
video dehazing/physics-prior/memory enhancement/image process/neural network/deep learning分类
信息技术与安全科学引用本文复制引用
林志鹏,秦佳,秦品乐,曾建潮..基于物理先验引导的记忆增强视频去雾算法[J].中北大学学报（自然科学版）,2025,46(6):726-733,8.基金项目
山西省揭榜挂帅重大专项(202101010101018) （202101010101018）
国家自然科学基金项目(62302466) （62302466）
山西省基础研究计划项目(202303021212188) （202303021212188）
