Present image-defogging methods have a range of problems: insufficient numbers of real datasets, local contrast imbalance, and defogging image distortion. This paper proposes a novel defogging network model (Densely Resnet with SKattention-Dehaze Net, DRS-Dehaze Net) that mitigates defogging image distortion. First, the fogged image is transformed into a multi-angle feature input map by the preprocessing module. The feature information is then extracted and redistributed through a dense residual architecture with an attention mechanism. Finally, the features are fused to output a fog-free image. Experimental comparison results confirmed a better defogging effect of the proposed algorithm than that of other algorithms. Our model effectively improves the distortion in defogged images and enhances the image clarity to a certain extent.