Motion Blurred Image Segmentation Based On Deep Learning

Image segmentation is an important research direction in the field of computer vision and plays an important role in image understanding.Image segmentation refers to classifying each pixel of an image in order to segment the image into several regions of similar nature.In the process of images acquiring,environmental factors such as the atmosphere and illumination,or human factors such as camera shake and inaccurate focus,will lead to degradation of image quality and blurring of images.Traditional segmentation method couldn't satisfy the segmentation requirements for such degraded images.With the gradual deepening of deep learning technology,more and more researchers have begun to use Convolutional Neural Network to solve problems in the field of computer vision,and many excellent models have emerged.However,a deep neural network model generally solves only one problem.In the face of the segmentation task of motion blurred images,two deep neural network model models are connected in series to complete the task of motion blurred image segmentation in this thesis.The main research work and innovations of this thesis are as follows:Firstly,a motion blurring algorithm based on Generative Adversarial Networks is proposed.This algorithm solves the problems of complex blur kernel estimation and low image restoration quality in the current motion blurring image restoration process.The deep learning concept is applied to the image restoration field,and the end-to-end motion deblurring algorithm model is realized.In terms of the deep learning model,considering that the sharp image is close to the corresponding pixel of the blurred image,the content loss and the Wasserstein distance are combined to constrain the internal features of the image in the loss function of the model,and the validity of the model is enhanced.In terms of network structure,the residual separable convolution module is designed as the Generative Model module according to the Residual Network and the Depthwise Separable Convolution.To ease the mosaic of the generated content,this thesis adds a bilinear interpolation to the generation network module.The global average pool layer is added to the Discriminative Model instead of the full connection layer,which can helps the structure of the whole network avoid over-fitting and greatly reduces the network parameters and makes the input space transformation more stable.According to the experimental analysis and optimization,a better recovery rate is obtained.Finally,the model is verified by experiments to have significant recovery effect.Secondly,the motion deblurring algorithm is combined with the latest semantic segmentation algorithm Deeplabv3+to solve the blur image segmentation problem.On the basis of in-depth research on key technologies such as Dilated Convolution,Spatial Pyramid Pooling and Atrous Spatial Pyramid Pooling,the Deeplabv3+semantic segmentation algorithm is implemented,and the segmentation effect of motion blurred image is verified.Then,the motion trajectory of the blur kernel is randomly generated by the Markov process,and the blur kernel is generated by applying the sub-pixel interpolation to the trajectory vector,thereby synthesizing the blur data set corresponding to the VOC2012 data set.The trained motion deblurring algorithm is migrated to the artificial blur data set,and the new trained motion deblurring algorithm is connected with the DeepLabv3+segmentation model.The experimental results verify the effectiveness of the idea.The proposed algorithm in this thesis has carried out a large number of experiments on GoPro dataset and VOC2012 dataset respectively,and compared with the existing algorithms,the experimental results has better motion deblurring effect and segmentation effect.