US 12,340,488 B2
Noise reconstruction for image denoising
Ioannis Marras, London (GB); Ioannis Alexiou, London (GB); Gregory Slabaugh, London (GB); and Stefanos Zafeiriou, London (GB)
Assigned to Huawei Technologies Co., Ltd., Shenzhen (CN)
Filed by HUAWEI TECHNOLOGIES CO., LTD., Guangdong (CN)
Filed on Jun. 3, 2022, as Appl. No. 17/831,578.
Application 17/831,578 is a continuation of application No. PCT/EP2019/083713, filed on Dec. 4, 2019.
Prior Publication US 2022/0301114 A1, Sep. 22, 2022
Int. Cl. G06T 5/70 (2024.01); G06N 3/088 (2023.01); G06T 5/50 (2006.01)
CPC G06T 5/70 (2024.01) [G06N 3/088 (2013.01); G06T 5/50 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20224 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A computer-implemented method for training a model to perform noise reduction on images, the method comprising:
receiving a plurality of training images;
receiving a plurality of noise signatures; and
for each of the plurality of training images:
(i) selecting one of the plurality of noise signatures and applying that noise signature to the respective training image to form a noisy input image;
(ii) forming a first noise estimate in the noisy input image by implementing a candidate version of the model on the noisy input image and forming an estimate of the respective training image by subtracting the first noise estimate from the noisy input image;
(iii) forming a second noise estimate by implementing the candidate version of the model on the respective training image and the selected noise signature; and
(iv) adapting the candidate version of the model in dependence on (a) a difference between the respective training image and the estimate of the respective training image and (b) a difference between the second noise estimate and the selected noise signature.