US 12,456,172 B2
Systems and methods for image denoising using deep convolutional networks
Zengli Yang, San Jose, CA (US); Long Bao, San Diego, CA (US); Shuangquan Wang, San Diego, CA (US); Dongwoon Bai, San Diego, CA (US); and Jungwon Lee, San Diego, CA (US)
Assigned to Samsung Electronics Co., Ltd., (KR)
Filed by Samsung Electronics Co., Ltd., Suwon-si (KR)
Filed on Oct. 25, 2022, as Appl. No. 17/972,961.
Application 17/972,961 is a division of application No. 17/010,670, filed on Sep. 2, 2020, granted, now 11,508,037.
Claims priority of provisional application 62/987,802, filed on Mar. 10, 2020.
Claims priority of provisional application 62/988,844, filed on Mar. 12, 2020.
Prior Publication US 2023/0043310 A1, Feb. 9, 2023
Int. Cl. G06T 5/70 (2024.01); G06N 3/045 (2023.01); G06N 3/084 (2023.01); G06T 5/30 (2006.01); G06T 5/50 (2006.01)
CPC G06T 5/70 (2024.01) [G06N 3/045 (2023.01); G06N 3/084 (2013.01); G06T 5/30 (2013.01); G06T 5/50 (2013.01); G06T 2207/20016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20224 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
computing noise data by subtracting, by a processing circuit, a noisy image from a corresponding ground truth image;
clustering, by the processing circuit, a plurality of noise values of the noise data based on intensity values of the corresponding ground truth image;
permuting, by the processing circuit, a plurality of locations of the noise values of the noise data within each cluster;
generating, by the processing circuit, a synthetic noise image based on the permuted locations of the noise values;
adding, by the processing circuit, the synthetic noise image to the corresponding ground truth image to generate a synthetic noisy image; and
augmenting an image dataset for training a neural network to perform image denoising with the synthetic noisy image.