Evaluating BM3D and NBNet: A Comprehensive Study of Image Denoising Across Multiple Datasets
<|reference_start|>Evaluating BM3D and NBNet: A Comprehensive Study of Image Denoising Across Multiple Datasets: This paper investigates image denoising, comparing traditional non-learning-based techniques, represented by Block-Matching 3D (BM3D), with modern learning-based methods, exemplified by NBNet. We assess these approaches across diverse datasets, including CURE-OR, CURE-TSR, SSID+, Set-12, and Chest-Xray, each presenting unique noise challenges. Our analysis employs seven Image Quality Assessment (IQA) metrics and examines the impact on object detection performance. We find that while BM3D excels in scenarios like blur challenges, NBNet is more effective in complex noise environments such as under-exposure and over-exposure. The study reveals the strengths and limitations of each method, providing insights into the effectiveness of different denoising strategies in varied real-world applications.<|reference_end|>
@article{kaviani2024evaluating,
    title={Evaluating BM3D and NBNet: A Comprehensive Study of Image Denoising
  Across Multiple Datasets},
    author={Ghazal Kaviani, Reza Marzban, Ghassan AlRegib},
    journal={arXiv preprint arXiv:2408.05697},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05697},
    primaryClass={eess.IV cs.CV}
}