Lightweight application of image deblurring technology based on DeblurGANv2
With the development of deep learning algorithms,people begin to study the use of deep learning models to solve the problem of image blurring.DeblurGANv2 with excellent performance has achieved good results in the field of image deblurring applications,but due to the complexity of the algorithm,the perform-ance requirements for model running devices are also higher.In order to be able to transplant the Deblur-GANv2 model to Android terminals for application,on the basis of studying the application of image deblur-ring technology based on the DeblurGANv2 model on the computer terminal,Python is used to convert the DeblurGANv2 model,reducing the size and computing time of the model,compressing the model size from 233MB to 13.2MB,making the model ultimately able to run on the Android system,and the running time is also reduced compared to the computer side model.The application provides ideas and solutions for the deployment of other deep learning algorithms to mobile terminals or low-performance devices.
image processingimage eblurringdepth learninggenerative adversarial networksmobile application development
万方数据
维普
