Method for training convolutional neural network to reconstruct an image and system for depth map generation from an image

A method for training a convolutional neural network to reconstruct an image. The method includes forming a common loss function basing on the left and right images (IL, IR), reconstructed left and right images (I′L, I′R), disparity maps (dL, dR), reconstructed disparity maps (d′L, d′R) for the left...

Full description

Saved in:
Bibliographic Details
Main Authors Turko, Sergey Alexandrovich, Shcherbinin, Andrey Yurievich, Anisimovskiy, Valery Valerievich
Format Patent
LanguageEnglish
Published 09.08.2022
Subjects
CALCULATING
COMPUTING
COUNTING
ELECTRIC COMMUNICATION TECHNIQUE
ELECTRICITY
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
PICTORIAL COMMUNICATION, e.g. TELEVISION
Online AccessGet full text

Cover

More Information
Summary:A method for training a convolutional neural network to reconstruct an image. The method includes forming a common loss function basing on the left and right images (IL, IR), reconstructed left and right images (I′L, I′R), disparity maps (dL, dR), reconstructed disparity maps (d′L, d′R) for the left and right images (IL, IR) and the auxiliary images (I″L, I″R) and training the neural network based on the formed loss function.
Bibliography:Application Number: US202017085081