Design And Implementation Of Text Image Restoration Svstem In Weak Network Video Conference Scene

In 2020,the COVID-19 has spread quietly,and the way people work and study has changed dramatically.Video conferencing is widely used in corporate office,government governance,online teaching and other scenarios in all walks of life.Users’ requirements for video conferencing quality are also gradually increasing.However,in weak network scenarios with poor network environments such as subways,elevators or remote mountainous areas,video upstream transmission will cause a serious packet loss rate,resulting in blurred text images such as ppt or documents that the host shares the screen when viewed by the participants.Clearly,the user experience is severely affected.With the application of deep learning in the field of images,it is possible to repair blurred images with image super-resolution algorithms.Common image super-resolution algorithms treat text images as general images,ignore the specific properties of text,and repair text details too smoothly.For the restoration of text images in video conferences,more attention should be paid to the recognizability of image text areas.Based on this,this paper mainly studies the text detection algorithm,which detects the blurred video image frame and cuts the text area image.Aiming at the blurred text area image,this paper conducts in-depth research on the text image super-resolution algorithm,and restores the clipped text image through the network to obtain a clearly identifiable text image.The main contents of this paper are as follows:Firstly,an attention-based text detection network is proposed.In view of the characteristics of long video conference text,deformable convolution is used in the convolution layer of the feature map extraction backbone network to expand the receptive field of the network,and a channel attention mechanism is introduced to suppress background noise and improve the robustness of the model.Through a large number of experimental comparisons,it is verified that the accuracy of the detection algorithm in this paper has been improved.Secondly,a novel text-image super-resolution network is proposed.Considering the characteristics of text images,BiLSTM is added to the information extraction and enhancement module to extract the sequence features of the text,and a local fusion structure is proposed for the residual module to fully learn the features of each layer,which improves the repair effect to a certain extent.Thirdly,in order to verify the usability of the proposed algorithm,a video conferencing system for text and image restoration is designed and implemented.Through this system,users can start the video conference process,and set the packet loss rate on the client to simulate the weak network situation,which proves the effectiveness of the text detection and text image super-resolution algorithm proposed in this paper.