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From Noise to Clarity: A Hybrid Approach for Image Denoising Using Traditional and Deep Learning Methods

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  • Spriha Deshpande

    (CA, USA)

  • Suman Basavaraj Mukkanagoudar

    (CA, USA)

Abstract

In this study, we explore various image denoising techniques to restore images affected by noise, with a particular focus on traditional and deep learning-based methods. The research compares conventional denoising approaches, including Wavelet Thresholding, Bilateral Filtering, Non-Local Means, and Wavelet Denoising with Bayesian Shrinkage, against state-of-the-art deep learning models, such as DnCNN and U-Net. The performance of these methods is evaluated based on two metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Additionally, we investigate the potential of Noise2Noise, a deep learning technique trained without clean images, to enhance the robustness of denoising in adverse weather conditions. The results indicate the strengths and weaknesses of both conventional and deep learning-based approaches, providing insights into their applicability in real-world image restoration tasks.

Suggested Citation

  • Spriha Deshpande & Suman Basavaraj Mukkanagoudar, 2020. "From Noise to Clarity: A Hybrid Approach for Image Denoising Using Traditional and Deep Learning Methods," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 2(2), pages 39-52, October.
    • Handle: RePEc:alk:jcsts0:v:2:y:2020:i:2:id:8793
    DOI: 10.32996/jcsts.2020.2.2.5
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