US 12,118,692 B2
Image super-resolution
Victor Kulikov, Moscow (RU)
Assigned to Picsart, Inc., Hallandale Beach, FL (US)
Filed by PICSART, INC., San Francisco, CA (US)
Filed on Dec. 8, 2021, as Appl. No. 17/544,981.
Claims priority of application No. 2020141817 (RU), filed on Dec. 17, 2020.
Prior Publication US 2022/0198610 A1, Jun. 23, 2022
Int. Cl. G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06T 3/4007 (2024.01); G06T 3/4046 (2024.01); G06T 3/4053 (2024.01)
CPC G06T 3/4053 (2013.01) [G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 3/4007 (2013.01); G06T 3/4046 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving input image data of a low-resolution image, having an input size, to generate output image data for a high-resolution image having an output size, the output size being greater than the input size;
interpolating the input image data to match the output size, thereby generating interpolated image data for an interpolated image of the output size;
determining residual image data based on the input image data, the determining the residual image data based on the input image data comprising:
providing the input image data to a first convolutional neural network (CNN) trained to estimate probability density of high-resolution indexes for the input image data to generate estimated probability density of intermediate indexes;
based on the estimated probability density of intermediate indexes, selecting a plurality of intermediate indexes;
providing the plurality of intermediate indexes as input to a second convolutional neural network to generate the residual image data;
combining the interpolated image data with the residual image data to generate the output image data for the high-resolution image of the output size.