一种基于多尺度卷积神经网络和分类统计的图像去雾霾方法

传统的去雾霾方法会导致天空、白云和明亮区域内的颜色失真.为了解决以上问题,提出了一种基于多尺度卷积神经网络和分类统计的去除图像雾霾的方法.首先用多尺度卷积神经网络估计图像的透射率,其次对所估计的透射率进行分类统计以确定在暗通道内天空、白云和明亮区域的像素值,最后通过低通高斯滤波器平滑图像场景的辐射度,得到恢复的无雾霾图像.实验结果表明,采用提出的方法对图像去雾霾后明亮区域内的颜色不会失真,且保留了图像的自然外观,对合成图像和真实图像均有较好的去雾霾效果.

Image Dehazing Method Based on Multi-scale Convolutional Neural Network and Classification Statistics

Traditional methods of image dehazing can distort color in areas such as the sky,white clouds,and bright areas.To address these problems,a three-step method is proposed for removing image hazing using a multi-scale convolutional neural network(MCNN)and classification statistics.First,the MCNN is used to estimate the transmittance of the image.Second,the estimated transmittance is classified and the pixel values of the sky,white clouds,and other bright regions in the dark channel are determined.Finally,the radiance of the scene is smoothed by a low-pass Gaussian filter to produce a restored haze-free image.Experimental results show that this method preserves the color in bright areas after the image is defogged,retaining the natural appearance of the image.The proposed method achieves improved dehazing on both synthetic and real images.