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DEEP LEARNING-BASED SYSTEM AND METHOD FOR AUGMENTING VARIOUS LOW-LIGHT IMAGE DATA, CAPABLE OF ADJUSTING BRIGHTNESS
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Title
DEEP LEARNING-BASED SYSTEM AND METHOD FOR AUGMENTING VARIOUS LOW-LIGHT IMAGE DATA, CAPABLE OF ADJUSTING BRIGHTNESS
Alternative Title
DEEP LEARNING-BASED LOW-LIGHT IMAGE AUGMENTATION SYSTEM FOR WITH SPECIFIC BRIGHTNESS AND METHOD OF THE SAME
Country
UN
Application Date
2024-05-07
Application No.
PCT/KR2024/006075
Registration Date
2024-12-05
Publication No.
2024248349
Assignee
DAEGU GYEONGBUK INSTITUTE OF SCIENCE AND TECHNOLOGY,재단법인대구경북과학기술원
URI
https://scholar.dgist.ac.kr/handle/20.500.11750/58836 PCT/KR2024/006075
Abstract
The present invention relates to a technology for performing low-light image data augmentation by converting a bright image such as a daytime situation into a low-light image such as a nighttime situation, and a low-light image data augmentation system according to an embodiment includes: a training data configuration unit for configuring training data on the basis of a first image generated in a first environment classified on the basis of illuminance and a second image generated in a second environment in low illumination compared to the first environment; an input image conversion unit for converting the first image into the second image and the second image into the first image, by receiving the first image and the second image as inputs; and a calculation unit for calculating a total loss for training a model by comparing an input image with at least one of the converted first image or the converted second image, wherein the input image conversion unit can convert the first image in the first environment into the second image in the second environment on the basis of a brightness degree (b) and a weight (w), through the model trained using the total loss.
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임용섭
Lim, Yongseob임용섭

Department of Robotics and Mechatronics Engineering

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