US 12,079,973 B2
Electronic devices and corresponding hybrid methods of low light image enhancement
Zhicheng Fu, Naperville, IL (US); Yunming Wang, Buffalo Grove, IL (US); Chao Ma, Evanston, IL (US); Joseph Nasti, Chicago, IL (US); and Hong Zhao, Naperville, IL (US)
Assigned to Motorola Mobility LLC, Chicago, IL (US)
Filed by Motorola Mobility LLC, Chicago, IL (US)
Filed on Aug. 20, 2021, as Appl. No. 17/408,075.
Claims priority of provisional application 63/226,444, filed on Jul. 28, 2021.
Prior Publication US 2023/0036222 A1, Feb. 2, 2023
Int. Cl. G06T 5/92 (2024.01); G06T 3/4046 (2024.01); G06T 3/4053 (2024.01)
CPC G06T 5/92 (2024.01) [G06T 3/4046 (2013.01); G06T 3/4053 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A hybrid method for low light image enhancement, the method comprising:
capturing, with an imager, a high resolution, low light image;
downsampling, with one or more processors operable with the imager, the high resolution, low light image to obtain a low resolution, low light image;
processing, by the one or more processors using a low light enhancement model of a deep neural network, the low resolution, low light image to obtain a low resolution, enhanced image; and
generating, by the one or more processors from a mathematical model, a high resolution, enhanced image from three inputs:
the high resolution, low light image;
the low resolution, low light image; and
the low resolution, enhanced image;
wherein:
the low light enhancement model is trained through deep learning of semantic enhancement for images photographed in a low light environment; and
the mathematical model comprises a sharpness-preserving mathematical model.