Research On Image Restoration,Enhancementand Registration For Railway Operating Environment Detection

With the increasing of railway running mileage,railway operating environment becomes more complex than before.Railway operating environment detection which is one of the effective measures to exclude potential security threats has been caused midespread concern.Railway operating environment detection mainly consists of the rail component detection and the railway forward motion environment detection.Thispaper takes the railway operating environment as the main research object,considering the influence of the image blur,non-uniform illumination and self-similar objects for railway operating environment,image restoration,image enhancement and image registration are used to solve these practical problems in railway operating environment detection.Wherein the image restoration and image enhancement theory and technique provide the clear detectable rail and bolt image;image registration makes it possible to detect the forward motion environment based on template comparison.The main innovationsoftheresearch are shown as following:First,we propose a regularization method based on convolution neural network,considering the effect of image blurring and noise on rail defect.This method overcomes the limitations of the traditional regularization method that use the same norm to restore the whole image,and takes norm selection of rail image restoration as a classification problem.According to convolutional neural network theory and application guidance,we select the corresponding forms of norm regularization constraints for different types of rail sub-block.Therefore,regularization method is no longer limited to simple single constraints hypotheses,which obtains better restored results and provides clearer input images for subsequent rail abrasion detection.The computational complexity of this method is much less than traditional regularization methods and it can realize the parallel processing,so this method lays a good foundation for subsequent rail defect detection.This method is suitable for the process of restoration in rail defect detection,and it applies to the natural image restoration after the transformation.The restored resultsare superior to other widely used regularization method for image restoration.Second,we propose a vision perception enhancement method based on local characteristics of image,considering nonuniform illumination(piecewise uniform illumination)influenceon railbolt detection.This method overcomes the limitation that the traditional Retinex enhancement methods choose the same kind of convolution kernel to estimate illumination in different bolt sub-images.Based on illumination piecewise uniform characteristics inside the scene,this method firstly uses image segmentation method to segment the partitions bolt image into different sub-images.The regions of sub-image are in the similar light conditions,while different sub-image are in different light conditions.According to the mean of Gaussian curvature we choose suitable Gaussian convolution kernel for the single sub-image to estimate illumination component,and then synthesized entire enhanced bolt image with illumination component and reflection component of the whole image.This method greatly improves the image enhancement effect and the accuracy of the subsequent missing bolt detection.The method after improvement(combined color restoration and color cast,etc.)has good universality,which can also be used to enhance other natural images and the enhancement resultsare superior to other widely used Retinex enhancement methods.Third,we propose a railway forward motion image registration method based on the depth information,considering railway forward motion image feature points matching is susceptible to interference from self-similarity objects in railway scene.This method overcomes the limitation of the traditional image registration which cannot be used in the registration of railway scene images with high self-similar objects.The method exploits the depth information of the image to guide railway forward motion image registration and divides railway forward motion image into the depth of scene and the depth of object.For the depth of scene,the sky part above the horizon in the image is set to infinity,and the ground part below the horizon has uniformly increasing depth from the bottom of the image to the horizon.For the depth of object,considering that the moving objects with different depth positions inside scene have different optical flow,we use the optical flow to approximate the depth.Guide filter is used to optimize depth map,in order to obtain higher accuracy depth estimation results of railway forward motion images.Finally,we construct D-SFIT feature descriptor to achieve accurate registration of self-similar objects in railway forward moving images,which lays the foundation for the future research on the railway forward operating environment detection.