摘要
Abstract
Current distortion correction techniques are based on the availability of references,for instance,straight lines and faces,and they have a limited range of applications.Some deep learning methods also focus only on a single distortion correction,and the correction effect is not good enough.Therefore,an image hybrid distortion correction method based on RepVGG network architecture is proposed to effectively eliminate multiple distortions widely existed in life.In the method,for the extracted features,a prediction model and a classification model are used to obtain the prediction data and the distortion type data,respectively,and a distortion correction model is trained based on the two sets of data,so as to realize the process of prediction,classification,and correction of the distorted images.The spatial attention mechanism is also introduced into the network to focus on the region of severe distortion.The nonlinear structure in the RepVGG block is replaced with a linear scaling layer,which allows the operations in the block to be merged during training,thus reducing the model training complexity.Then the multi-branch structure is converted into a single convolutional layer by compressing blocks,which not only accelerates the training process,but also maintains the expression advantage of the multi-branch structure.The model is experimentally demonstrated to have excellent correction ability on six types of distorted images.In comparison with the existing algorithms,the correction rate of the proposed algorithm can reach above 98%,and the training speed of the model is improved by about 1.53 times.To sum up,this method has a certain application in the field of image distortion correction.关键词
图像畸变矫正/深度学习/混合畸变/RepVGG/空间注意力机制/多分支结构Key words
image distortion correction/deep learning/hybrid distortion/RepVGG/spatial attention mechanism/multi-branch structure分类
电子信息工程
