US 11,704,771 B2
Training super-resolution convolutional neural network model using a high-definition training image, a low-definition training image, and a mask image
Yunchao Zhang, Beijing (CN); Shuai Chen, Beijing (CN); Zhiping Jia, Beijing (CN); and Lei Miao, Beijing (CN)
Assigned to HUAWEI TECHNOLOGIES CO., LTD., Shenzhen (CN)
Appl. No. 16/759,870
Filed by Huawei Technologies Co., Ltd., Shenzhen (CN)
PCT Filed Dec. 1, 2017, PCT No. PCT/CN2017/114181
§ 371(c)(1), (2) Date Apr. 28, 2020,
PCT Pub. No. WO2019/104705, PCT Pub. Date Jun. 6, 2019.
Prior Publication US 2020/0334789 A1, Oct. 22, 2020
Int. Cl. G06T 3/40 (2006.01); H04N 23/69 (2023.01)
CPC G06T 3/4053 (2013.01) [G06T 3/4046 (2013.01); H04N 23/69 (2023.01)] 20 Claims
OG exemplary drawing
 
1. An image processing method implemented by a terminal, comprising:
training a super-resolution convolutional neural network model using a high-definition training image, a low-definition training image, and a mask image to obtain a first target super-resolution convolutional neural network model;
enabling a photographing function of the terminal;
enabling a zoom function of the terminal;
receiving a selection input of a user;
determining a target zoom magnification based on the selection input;
collecting a to-be-processed image;
processing the to-be-processed image using the first target super-resolution convolutional neural network model to obtain a processed image corresponding to the target zoom magnification by:
identifying that the target zoom magnification is greater than a maximum optical zoom magnification of the terminal; and
processing, in response to the identifying, the to-be-processed image using the first target super-resolution convolutional neural network model; and
displaying the processed image.