Research Article
Research on Preprocessing Process for Improved Image Generation Based on Contrast Enhancement
@INPROCEEDINGS{10.1007/978-3-031-52265-9_10, author={Tae-su Wang and Minyoung Kim and Cubahiro Roland and Jongwook Jang}, title={Research on Preprocessing Process for Improved Image Generation Based on Contrast Enhancement}, proceedings={Big Data Technologies and Applications. 13th EAI International Conference, BDTA 2023, Edinburgh, UK, August 23-24, 2023, Proceedings}, proceedings_a={BDTA}, year={2024}, month={1}, keywords={Contrast enhancement CLAHE SSIM PSNR LoFTR}, doi={10.1007/978-3-031-52265-9_10} }- Tae-su Wang
Minyoung Kim
Cubahiro Roland
Jongwook Jang
Year: 2024
Research on Preprocessing Process for Improved Image Generation Based on Contrast Enhancement
BDTA
Springer
DOI: 10.1007/978-3-031-52265-9_10
Abstract
Lighting conditions in daytime environments can reduce the object recognition rate by causing blurring, over-exposure, and shadows that mask important information about the object's shape and size. These phenomena also decrease the quality of image data, with outdoor quality being significantly lower than indoor quality. As deep learning-based object recognition algorithms heavily rely on image quality, a preprocessing process is required to improve the quality of learning image data and achieve high performance. To address this, the paper proposes a contrast-enhanced image generation preprocessing process that can improve image quality and mitigate the effects of poor lighting conditions.
Copyright © 2023–2025 ICST
