Research On Infrared Image Colorization Algorithm For Vehicle

With the rapid development of the automobile industry,the occurrence of traffic accidents is also on the upward trend.Automobile manufacturers pay special attention to driving safety issues,and actively develop a variety of car-assisted driving system to improve driving safety.Driver Assistant System(DAS)utilizes an infrared camera to help the driver accurately determine the road conditions at night and in bad weather to avoid dangerous situations.However,the resolution and contrast of infrared images are blurry,and the visualization and observation comfort are not sufficient.Therefore,it becomes very important to perform colorization.With the problem of copious manually scribble and reference image selection of previous algorithm,we presents a full automatic colorization method based on multi-scale convolutional neural network,completing experiment plot installation,dataset foundation,image pre-processing,convolutional neural network model design,loss function selection,neural network training and test etc.Due to training neural network depends a mass data,and there's little dataset include night-time color image and infrared image at the same time now.So we set up a nocturnal urban road dataset with infrared image and its corresponding color image by FILR thermal imaging system and Nikon digital camera.Because night-time image taken in low light condition and difficult to observe,we improved nonlinear piecewise adjustment function to enhance the luminance.Compared with the existing processing algorithms,our method not only improving the image quality but ensuring that the entire picture is natural without distortion.In the meanwhile,considering low resolution and poor contrast of infrared image in the training set.We proceed infrared image super-resolution reconstruction utilizing convolutional neural network.After enhancement,infrared image will include more details,which provide high-quality and distinct data for training neural network.We proposed a colorization algorithm based on convolutional neural network.It uses the night color image after brightness enhancement and single image superresolution reconstruction of the infrared image as input to train the design of multiscale convolutional neural network.Let it learn the infrared image and its corresponding color image mapping relationship directly,and finally get the color of the image.In the meanwhile,we elaborates on the network model,the selection and establishment of the experimental data set,the training process and the results of the analysis in detail.Compared with other common coloring methods,convolutional neural network can automatically extract features and avoid human participation.The experimental results show that the color comes our method is more natural after colorization processing,and the target recognition in the image is significantly improved.At the same time,its processing speed is faster and suitable for car image colorization processing.