US 12,272,032 B2
Machine learning-based approaches for synthetic training data generation and image sharpening
Devendra K. Jangid, Santa Barbara, CA (US); John Seokjun Lee, Allen, TX (US); and Hamid R. Sheikh, Allen, TX (US)
Assigned to Samsung Electronics Co., Ltd., Suwon-si (KR)
Filed by Samsung Electronics Co., Ltd., Suwon-si (KR)
Filed on Aug. 18, 2022, as Appl. No. 17/820,795.
Prior Publication US 2024/0062342 A1, Feb. 22, 2024
Int. Cl. G06T 5/70 (2024.01)
CPC G06T 5/70 (2024.01) [G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 26 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining an input image that contains blur;
providing the input image to a trained machine learning model, the trained machine learning model comprising (i) a shallow feature extractor configured to extract one or more feature maps from the input image and (ii) a deep feature extractor configured to extract deep features from the one or more feature maps; and
using the trained machine learning model to generate a sharpened output image;
wherein the trained machine learning model is trained using ground truth training images and input training images, the input training images comprising versions of the ground truth training images with blur created using demosaic and noise filtering operations.