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• We present the Multi-scale Recurrent Attention Gated Fusion Network (MRAGFN) as an end-to-end solution for tackling the image dehazing task. • We develop the Global Feature Extraction Module (GFEM), which utilizes dilated convolutions of different receptive fields to effectively extract complex features from the input image. • We introduce the Dual Attention Fusion (DAF) module to effectively implement the attention mechanism and create haze-relevant feature maps. • We design the Feature Enhancing Module (FEM), drawing inspiration from the SOS boosting algorithm. • We propose the Recurrent Attention Gated Fusion (RAGF) module, which incorporates attention and gating mechanisms. This module progressively refines features by giving weight to relevant information while reducing redundancy. The purpose of single image dehazing is to eliminate the bad influence of haze on images, so as to maintain more scene information of images. In recent years, the convolutional neural networks (CNN) have made significant contributions to single image dehazing. However, the visual quality of dehazed images still needs to be further improved. In view of the problems of single-scale shallow image feature extraction and the insufficient use of intermediate layer features in existing dehazing networks, we propose an end-to-end Multi-scale Recurrent Attention Gated Fusion Network (MRAGFN) to address the image dehazing task. We cascade three Dual Attention Fusion (DAF) modules to progressively form three haze-relevant features map, meanwhile, we adopt downsampling operation on the input to produce global feature map, which are used to weight the three feature maps to compensate for the missing of single-scale feature information. We present Feature Enhancement Module (FEM) to enhance the feature representation ability of these weighted feature maps. We design Recurrent Attention Gated Fusion (RAGF) module by adding attention mechanism and gating mechanism to gradually obtain more refined features based on these weighted features while eliminating redundant features. Experimental results on different hazy images demonstrate that the proposed dehazing network can restore the haze-free images and perform better than the state-of-the-art dehazing networks in terms of the objective indicators (such as PSNR, SSIM) and the subjective visual quality. [ABSTRACT FROM AUTHOR]