Abstract: This application provides a method for generating a high-resolution picture performed by a computer device. The method includes: acquiring at least one deep neural network model; acquiring a low-resolution picture; determining a corresponding deep neural network model according to the low-resolution picture; and converting the low-resolution pictures into a high-resolution picture through the deep neural network model, the deep neural network model including a plurality of non-linear conversion convolution layers that alternately use different parameter matrices as convolution template parameters.

Abstract: This application provides a method for generating a high-resolution picture performed by a computer device. The method includes: acquiring at least one deep neural network model; acquiring a low-resolution picture; determining a corresponding deep neural network model according to the low-resolution picture; and converting the low-resolution pictures into a high-resolution picture through the deep neural network model, the deep neural network model including a plurality of non-linear conversion convolution layers that alternately use different parameter matrices as convolution template parameters.

Abstract: This disclosure relates to a method, device and user equipment for pushing service information. The method comprises: monitoring a status of a call; determining whether to trigger a push of the service information based on the status of the call; acquiring at least one piece of the service information and a corresponding pushing manner based on the status of the call; and pushing at least one piece of the service information to a current call status interface. The present disclosure enables accurate pushing of service information to a user by taking into account the progress of the call.