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Big Data Technologies and Applications. 13th EAI International Conference, BDTA 2023, Edinburgh, UK, August 23-24, 2023, Proceedings

Research Article

Research on Preprocessing Process for Improved Image Generation Based on Contrast Enhancement

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BibTeX Plain Text
  • @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
Tae-su Wang1, Minyoung Kim1, Cubahiro Roland1, Jongwook Jang1,*
  • 1: Dong-eui University
*Contact email: jwjang@deu.ac.kr

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.

Keywords
Contrast enhancement CLAHE SSIM PSNR LoFTR
Published
2024-01-31
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-52265-9_10
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