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.
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Acknowledgement
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2023–2016-0–00318) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).
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Wang, Ts., Kim, M., Roland, C., Jang, J. (2024). Research on Preprocessing Process for Improved Image Generation Based on Contrast Enhancement. In: Tan, Z., Wu, Y., Xu, M. (eds) Big Data Technologies and Applications. BDTA 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 555. Springer, Cham. https://doi.org/10.1007/978-3-031-52265-9_10
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