IMAGE HARMONIZATION FOR DEEP LEARNING MODEL OPTIMIZATION

Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmoni...

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Bibliographic Details
Main Authors TOROK, Levente Imre, TEGZES, Pal, YOUNIS, Khaled, TAN, Tao, FERENCZI, Lehel, AVINASH, Gopal B, GHOSE, Soumya, RUSKO, Laszlo, RAO, Gireesha Chinthamani
Format Patent
LanguageEnglish
French
German
Published 08.03.2023
Subjects
CALCULATING
COMPUTING
COUNTING
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
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Summary:Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmonizing the sub-images with corresponding reference sub-images of at least one reference image based on two or more different statistical values respectively calculated for the sub-images and the corresponding reference-sub images, resulting in transformation of the sub-images into modified sub-images images. In some implementations, the modified sub-images can be combined into a harmonized image having a more similar appearance to the at least one reference image relative to the input image. In other implementations, harmonized images and/or modified sub-images generated using these techniques can be used as ground-truth training samples for training one or more deep learning model to transform input images with appearance variations into harmonized images.
Bibliography:Application Number: EP20210719418