一种改进的轻量型网络图像去雾方法OA

To address the issues of high computational complexity and large parameter size in convolutional neural network(CNN)-based image dehazing,this study proposes a lightweight dehazing network(LDNet).First,the atmospheric scattering model is reformulated to directly suppress haze noise,thereby reducing cumulative errors in intermediate variable estimation.Second,a reverse residual network module with an attention mechanism(RNAM)is designed to extract multi-scale features while emphasizing critical semantic information,effectively reducing model complexity and parameter size.Finally,a joint loss function combining L1 smoothing loss and multi-scale structure similarity(MS-SSIM)loss is used to improve reconstruction quality.The experimental results show that the proposed method outperforms existing approaches in terms of structural similarity and peak signal-to-noise ratio(PSNR)on synthetic datasets,while also achieving effective dehazing performance on real-world images.In addition,the model exhibits reduced parameter size and improved computational efficiency.