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.