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A New Multi-level Hazy Image and Video Dataset for Benchmark of Dehazing Methods

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Fourth International Conference on Image Processing and Capsule Networks (ICIPCN 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 798))

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Abstract

The changing level of haze is one of the main factors which affects the success of the proposed dehazing methods. However, there is a lack of controlled multi-level hazy dataset in the literature. Therefore, in this study, a new multi-level hazy color image dataset is presented. Color video data is captured for two real scenes with a controlled level of haze. The distance of the scene objects from the camera, haze level, and ground truth (clear image) is available so that different dehazing methods and models can be benchmarked. In this study, the dehazing performance of five different dehazing methods/models is compared on the dataset based on SSIM, PSNR, VSI, and DISTS image quality metrics. Results show that traditional methods can generalize the dehazing problem better than many deep learning-based methods. The performance of deep models depends mostly on the scene and is generally poor on cross-dataset dehazing.

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Acknowledgements

This study is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) within the Project number: 122E333.

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Authors and Affiliations

  1. Computer Engineering, Ted University, Ankara, Turkey

    Bedrettin Çetinkaya, Yücel Çimtay, Fatma Nazli Günay & Gökçe Nur Yılmaz

Authors
  1. Bedrettin Çetinkaya
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  2. Yücel Çimtay
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  3. Fatma Nazli Günay
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  4. Gökçe Nur Yılmaz
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Corresponding author

Correspondence to Bedrettin Çetinkaya .

Editor information

Editors and Affiliations

  1. Department of Electronics and Computer Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Lalitpur, Nepal

    Subarna Shakya

  2. Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Universidad de Castilla-La Mancha, Albacete, Albacete, Spain

    Antonio Fernández-Caballero

  4. Department of Computer Science, International Hellenic University, Kavala, Greece

    George Papakostas

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Çetinkaya, B., Çimtay, Y., Günay, F.N., Yılmaz, G.N. (2023). A New Multi-level Hazy Image and Video Dataset for Benchmark of Dehazing Methods. In: Shakya, S., Tavares, J.M.R.S., Fernández-Caballero, A., Papakostas, G. (eds) Fourth International Conference on Image Processing and Capsule Networks. ICIPCN 2023. Lecture Notes in Networks and Systems, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-99-7093-3_18

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