基于深度卷积神经网络的自适应图像去雾算法

室外拍摄图像由于受雾气、雾霾、沙尘等大气颗粒杂质的影响呈现出图像灰白化,而现有的图像去雾算法存在过度依赖先验信息、透射率计算不精确的问题。针对上述问题,文中提出了一种基于深度卷积神经网络的自适应图像去雾算法。该算法基于大气散射模型实现了有雾图像的去雾,设计浅层提取、并行提取和深度融合共3个全卷积网络实现图像浅层特征与深层特征的融合,大幅提高了透射率图的准确性。去雾实验测试结果表明,文中所提出的算法对室外露天雾图具有良好的去雾效果,且去雾细节效果更加理想。

Adaptive Image Dehazing Algorithm Based on Deep Convolutional Neural Network

Due to the influence of air particles such as fog, haze, dust and so on, the image of outdoor photography was gray and white. However, the existing image de fogging algorithm had the problems of over dependence on prior information and inaccurate transmission calculation. In order to solve the above problems, an adaptive image defogging algorithm based on the depth convolution neural network was proposed in this paper. The algorithm realized the defogging of the foggy image based on the atmospheric scattering model. Three full convolution networks including shallow extraction, parallel extraction and deep fusion, were designed to realize the fusion of the shallow and deep features of the image, which greatly improved the accuracy of the transmittance image. The experimental results showed that the algorithm proposed in this paper had a good defogging effect on outdoor fog map, and the effect of defogging details was better.