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Low Light Image Enhancement on Mobile Devices by Using Dehazing

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Electrical and Computer Engineering (ICECENG 2022)

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Abstract

The images which are captured in indoors and/or outdoors may be badly impacted when sufficient light does not exist. The pictures’ low dynamic range and high noise levels may have an impact on the overall success of computer vision systems. Computer vision applications become more powerful in low light situations when low light picture augmentation approaches are used to boost image visibility. Low light photos have a histogram that is very similar to hazy photographs. As a result, haze reduction techniques can be utilized to increase low light photo contrast. An image improvement approach based on inverting low lighting images and applying picture dehazing with an atmospheric light scattering model is suggested in this paper. The suggested technique has been implemented on the Android operating system. The proposed method delivers about 3 frames per second for 360p video on the Android operating system. It is extremely feasible to increase this real-time performance by employing more powerful hardware.

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Authors and Affiliations

  1. Ted University, Cankaya, 06420, Ankara, Turkey

    Yücel Çimtay & Gokce Nur Yilmaz

Authors
  1. Yücel Çimtay
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  2. Gokce Nur Yilmaz
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Correspondence to Yücel Çimtay .

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Editors and Affiliations

  1. Bandırma Onyedi Eylül University, Bandirma, Turkey

    Muhammet Nuri Seyman

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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Çimtay, Y., Yilmaz, G.N. (2022). Low Light Image Enhancement on Mobile Devices by Using Dehazing. In: Seyman, M.N. (eds) Electrical and Computer Engineering. ICECENG 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 436. Springer, Cham. https://doi.org/10.1007/978-3-031-01984-5_5

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  • DOI: https://doi.org/10.1007/978-3-031-01984-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-01983-8

  • Online ISBN: 978-3-031-01984-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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