基于改进AOD-Net的图像去雾算法

Image defogging algorithm based on improved AOD-Net

为了更好解决图像去雾后颜色失真、去雾不彻底和耗时等问题,提出了一种基于改进AOD-Net的图像去雾算法。首先,在原有的卷积模块中引入残差连接,并保留了第二个特征融合层第一层的特征信息,以增强网络的特征提取能力。其次,在第三个特征融合层后引入注意力模块,强化雾图中的关键特征信息,抑制无关背景干扰。最后,采用新的复合损失函数进行训练。实验结果表明,改进算法在公共数据集上的峰值信噪比提高了3.8 dB,结构相似性达到了93.6%。相较于其他去雾算法,该算法在去雾精度和处理效率方面均表现出色。

To address issues such as color distortion,incomplete defogging,and computational inefficiency in image defogging,this study proposes an improved image defogging algorithm based on the enhanced AOD-Net.Initially,a residual connection is introduced into the existing convolutional module,preserving the features of the first layer in the second feature fusion layer to enhance feature extraction capabilities.Subsequently,an attention module is introduced after the third feature fusion layer to strengthen the representation of crucial features in hazy images and suppress irrelevant background interference.Finally,a novel composite loss function is employed for training.Experimental results demonstrate that the proposed algorithm achieves a 3.8 dB improvement in Peak Signal-to-Noise Ratio(PSNR)and a structural similarity(SSIM)of 93.6%on a public dataset.Compared to other defogging algorithms,this algorithm exhibits superior performance in both defogging accuracy and processing efficiency.