US 11,721,000 B2
Image denoising method and apparatus, electronic device and non-transitory computer readalble storage medium
Fengshuo Hu, Beijing (CN)
Assigned to BOE Technology Group Co., Ltd., Beijing (CN)
Filed by BOE Technology Group Co., Ltd., Beijing (CN)
Filed on Jun. 23, 2021, as Appl. No. 17/355,938.
Claims priority of application No. 202010729938.2 (CN), filed on Jul. 27, 2020.
Prior Publication US 2022/0028041 A1, Jan. 27, 2022
Int. Cl. G06T 5/00 (2006.01); G06N 3/088 (2023.01); G06T 3/40 (2006.01); G06T 5/20 (2006.01); G06T 5/50 (2006.01); G06N 3/045 (2023.01)
CPC G06T 5/002 (2013.01) [G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06T 3/4046 (2013.01); G06T 3/4053 (2013.01); G06T 5/20 (2013.01); G06T 5/50 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 18 Claims
OG exemplary drawing
 
1. An image denoising method, comprising:
acquiring a first data set and a second data set, wherein the first data set comprises a plurality of first images without noise, the second data set comprises a plurality of second images with real noise, contents of each first image and each second image are different;
training, by using the first data set and the second data set, a first network to obtain a noise generation model;
inputting the first image into the noise generation model, and outputting a third image with simulated noise, wherein a plurality of third images forms a third data set; and
training, by using the first data set and the third data set, an image denoising network to obtain an image denoising model;
wherein the image denoising model is configured to convert an original image with noise into an output image without noise;
wherein prior to the inputting the first image into the noise generation model, the method further comprises:
converting the first image into a first training sample image;
the inputting the first image into the noise generation model and outputting the third image with simulated noise, comprises:
inputting the first training sample image into the noise generation model and outputting the third image, wherein a resolution of the first image is larger than a resolution of the first training sample image, and a resolution of the third image is the same as the resolution of the first training sample image;
wherein the image denoising model is further configured to convert the original image with noise and having a first resolution into the output image without noise and having a second resolution, and the first resolution is smaller than the second resolution.