A review of image denoising techniques: From classical filtering to deep learning and diffusion models

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Key Points

The aim is to review advancements in image denoising techniques and their implications for image quality.
Reviewed techniques from statistical filtering to deep learning and diffusion models.
Analyzed various noise models like Gaussian and salt-and-pepper noise.
Compared performance of classical and modern approaches using metrics like PSNR and SSIM.
Discussed future directions including hybrid models and self-supervised learning.
Identified strengths and limitations of different denoising techniques.
Confirmed that modern deep learning methods outperform classical techniques in certain scenarios.
Highlighted the importance of quality metrics like PSNR and SSIM in evaluating performance.

This review examines image denoising techniques in digital imaging, suggesting improved quality and reliability methods.

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Kim et al. (2026) studied this question.

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