Multi-scale Feature Fusion Image Dehazing Algorithm Combined with Attention Mechanism

Abstract

Aiming at the problems that traditional image dehazing algorithms are easily restricted by prior knowledge and color distortion,a multi-scale feature fusion image dehazing algorithm combined with attention mechanism is proposed.The algorithm first obtains feature maps of multiple scales through down-sampling operations,and then uses skip connections between feature maps of different scales to connect the feature maps of the encoder part and the decoder part for feature fusion.At the same time,a feature attention module composed of channel attention submodule and pixel attention submodule is added to the network to control the importance of different channels and pixels.This feature attention module allows the network to pay more attention to detailed information and important features,therefore,a better dehazing effect can be achieved.In order to verify the effectiveness of the proposed algorithm,qualitative and quantitative comparative experiments are carried out on the RESIDE dataset with five popular dehazing algorithms.The experimental results show that the proposed algorithm can more completely dehazing,and the preservation of image color is better.At the same time,the average values on the two evaluation indicators PSNR (Peak Signal to Noise Ratio) and SSIM (Structure Similarity) are 28.83 dB and 0.957 5,respectively,which are 2.23 dB and 0.017 2 higher than the second-performance model in the comparison algorithm.Then,qualitative comparison experiments are performed between the proposed algorithm and five comparison algorithms on the MSD data set and real images.The experimental results further prove that the proposed algorithm has good dehazing performance and color retention.