基于注意力的图像去雾算法探索研究

Research on Attention-Based Image Defogging Algorithm

图像去雾的方法可粗略划分为基于图像增强的去雾算法、基于物理模型的去雾算法、基于深度学习的去雾算法3类。将注意力机制引入传统的暗通道先验去雾算法,可克服该算法存在的某些不足。经MATLAB平台进行实验仿真,并利用主观方法(主观感知)与客观方法(峰值信噪比)对比分析仿真结果,证明引入注意力机制的暗通道先验去雾改进算法具有优化传统暗通道先验算法的效果。

The methods of image defogging can be roughly divided into three categories:Image enhancement based defogging algorithms,physical model-based defogging algorithms,and deep learning based dehazing algorithms.By introducing attention mechanisms into traditional dark channel prior defogging algorithms,certain shortcomings of this algorithm can be overcome.Experimental simulation was conducted on the MATLAB platform,and the simulation results were compared and analyzed using subjective methods(subjective perception)and objective methods(peak signal-to-noise ratio)to demonstrate that the improved dark channel prior defogging algorithm with attention mechanism has the effect of optimizing traditional dark channel prior algorithms.