Study On Image Dehazing Based On Convolutional Neural Network

On hazy days,scene information will be whittled down before it transmits to imaging device,which results the image scene a low visibility,low saturation and unclear details.Therefore,foggy image will bring difficulty to human analysis or automated computer analysis.The technique of dehazing process is helpful to improve image clarity,highlight the details of the scene,greatly enhance the quality of the image.In order to guarantee the quality of data for the achievement of relevant tasks,it is of great theoretical and practical value to study the restoration of foggy image.This paper first goes into details on the theories of foggy imaging model and neural network.Then we put forward a foggy image detecting algorithm based on convolutional neural network,and through which to judge whether an image is with haze or not.Then,for foggy images,this paper presents a transmission map estimation algorithm based on convolutional neural network to obtain the corresponding transmission map of a foggy image.At the end,we raise an improved airlight and transmission adjustment algorithm.By applying the algorithms mentioned above,the image dehazing algorithm gets better result of image dehazing.In this paper,several synthesized data set is employed to launch foggy image detection network training,which has achieved a high accuracy in both synthetic data set and real data set.Through transmission map estimation network training on synthetic data set,the final network with foggy image estimation can get more accurate transmission map.By combining airlight calculating algorithm and transmission adjustment algorithm in image dehazing,image clarity is improved and in the same time,side effects are suppressed.