针对当前去雾算法中容易产生的图像伪影、图像颜色失真、图像细节模糊不清等问题，提出一种并行多尺度注意力映射图像去雾算法，通过端到端方式以编码器解码器结构实现图像去雾。在编码阶段，采用连续下采样层降低特征维度，避免过拟合。在特征转换阶段，采用并行分支结构设计并行多尺度注意力映射模块，使模型能够在关注图像重要特征的同时充分利用多尺度特征，并通过并行连接选择性特征融合模块有效收集图像空间结构信息。解码阶段，采用上采样层重构图像，并通过上下采样层融合更好地保留图像边缘信息。实验结果表明，该算法在合成雾天数据集以及真实雾天图像上均具有较好的去雾效果，相较于传统去雾算法，可更好地保留图像细节，具备较好的色彩保持度。

Problems such as image color distortion, blurred image details, and image artifacts are prone to occur in the current dehazing algorithm. In order to solve the above problems, an image dehazing algorithm with parallel multi scale attention mapping is proposed. The algorithm achieves image defogging through an end-to-end encoder decoder structure. In the encoder stage, the continuous downsampling layer is used to reduce feature dimension and avoid over-fitting. In the feature transformation stage, a parallel multi scale attention mapping block with a parallel branch structure is designed, so that the model can make full use of multi scale features while focusing on important features of the image, and effective collection of image spatial structure information by connecting selective feature fusion block in parallel. In the decoding stage, the upsampling layer is used to reconstruct the image, and through skip connections of up and down sampling to better preserve image edge information. Experimental results show that the algorithm has better dehazing effects on both synthetic hazy datasets and real hazy images. Compared with traditional dehazing methods, this algorithm better preserves image details and has better color retention.