Abstract <p>Currently, there is a lack of regulatory safety in scenarios with haze or dense water mist. To address this issue, this study analyzes image dehazing technology and proposes an image dehazing model based on multiscale residual and mixed attention mechanism. This model improves image processing efficiency and image dehazing effect by combining multiscale residual networks with spatial attention, channel attention, and frequency attention. The model achieved peak signal-to-noise ratios of 35.76 and 34.39 dB, respectively, and structural similarity values of 0.9891 and 0.9870 in the indoor and outdoor test sets of the RESIDE dataset, which were significantly better than other comparison methods. In the NTIRE’18 test set, the model found the optimal peak signal-to-noise ratio of 12.26 dB in the 45th iteration, and the optimal similarity value of 0.684 in the 60th iteration. The application analysis in real-world task test sets showed that the research model had better visual effects and detail restoration ability. Time complexity analysis showed that the model had a lower runtime, indicating its efficient computational performance. The proposed model exhibits excellent dehazing performance and computational efficiency on multiple standard and real-world test sets, verifying the effectiveness of multiscale residual networks and mixed attention mechanisms in image dehazing tasks.</p>