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Deep Learning-Based Noise Type Classification and Removal for Drone Image Restoration

  • Research Article-Computer Engineering and Computer Science
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Abstract

Recent advancements in deep learning have enabled significant progress in image noise type classification and denoising systems. Researchers working on deep learning-based image multi-type denoising either use a single-stage or a two-stage denoising approach. The single-stage approach proposes designing a single denoising autoencoder (DAE), whereas the two-stage approach first classifies the noise type, followed by applying a noise-specific filter. The problem with the single-stage approach is that a generalized DAE fails to be effective. Two-stage approaches work on a limited number of noise types, as researchers typically address only two or three noise types. This paper proposes a framework for two-stage multi-type image denoising that provides classification and denoising of four types of noise with a per-class classification accuracy of 98.2–100% and a denoising technique that obtained promising PSNR and SSIM values for various types of noise, ensuring effective image restoration. The proposed methodology can be applied to any field that requires image denoising without prior knowledge of the type of noise.

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Data Availability

Data available in Github repository.

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Funding

This research work was funded by Institutional Fund Projects under Grant No. (IFPIP:169-611-1443). The authors gratefully acknowledge the technical and financial support by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.

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Authors and Affiliations

  1. Department of Computer Science, School of Engineering, Central Asian University, Tashkent, Uzbekistan

    Sajid Khan

  2. Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan

    Waqar Ahmed, Sajid Khan & Ghulam Mujtaba

  3. Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 80221, Saudi Arabia

    Adeeb Noor

Authors
  1. Waqar Ahmed
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  2. Sajid Khan
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  3. Adeeb Noor
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  4. Ghulam Mujtaba
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Contributions

Conceptualization and methodology, SK and WA; experiments, WA and SK; validation, WA and AN; writing-review and editing, WA, SK, and GM; project administration and funding acquisition, AN. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Adeeb Noor.

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The authors declare no conflict of interest.

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Ahmed, W., Khan, S., Noor, A. et al. Deep Learning-Based Noise Type Classification and Removal for Drone Image Restoration. Arab J Sci Eng 49, 4287–4306 (2024). https://doi.org/10.1007/s13369-023-08376-6

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  • DOI: https://doi.org/10.1007/s13369-023-08376-6

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