An image dehazing algorithm based on improved multi-scale AOD-Net

To address the current issues of inefficient dehazing algorithms and poor detail recovery, we propose an improved multi-scale AOD-Net (all in one dehazing network) algorithm. This algorithm enhances the network’s feature extraction and recovery capabilities through three key improvements: adding an attention mechanism, adjusting the network structure, and modifying the loss function. Specifically, the first layer of the model incorporates the SPA (spatial pyramid attention) mechanism, which enables the network to avoid redundant information during feature extraction. Furthermore, the network structure is modified into a Laplacian pyramid structure, allowing the model to extract features at different scales and preserve high-frequency information in the feature maps. Additionally, the original loss function is replaced with the MS-SSIM (multi-scale structural similarity)+L1 loss function, thereby enhancing the model’s ability to retain structural information. Experimental results demonstrate that this method achieves better dehazing effects and richer details. Subjectively, the dehazed images exhibit superior quality compared to those produced by the original network. Objectively, compared to the original network, there is a 2.55 dB improvement in PSNR, a 0.04 increase in SSIM value, and a 0.18 increase in IE entropy value, which proves the algorithm’s excellent dehazing effect and stability.