To address the common issues in most existing image dehazing algorithms,such as color distortion,haze residue,and blurring of texture details due to the loss of fine details,a new end-to-end dehazing algorithm based on multiresidual and multi-feature fusion is proposed.Initially,a shallow feature extraction module is designed to provide the deep network with feature maps rich in information.Subsequently,a multi-residual cascading module is constructed to extract multi-level features,assisting the model in learning more complex feature representations.Furthermore,a local-global feature fusion module is introduced to capture features ranging from the most subtle to the most extensive. Finally,a cross-layer feature fusion module,combined with residual attention,is designed to prevent the loss of details after upsampling and downsampling,thus better extracting local and global information features from the image. Experimental results show that the proposed algorithm achieves peak signal-to-noise ratio(PSNR)of 33.12 and 31.07 dB,and structural similarity(SSIM)of 0.986 and 0.983,respectively,on indoor and outdoor SOTS test sets, which is significantly improved compared with most current mainstream algorithms.Moreover,the fog removal effect is good in both the synthetic fog image and the real haze image,and the details of the restored image are clearer and more in line with human visual perception