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Overview of Research on Digital Image Denoising Methods
Jing Mao1, Lianming Sun2, Jie Chen3
1Graduate School of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan.
Sensors (Basel, Switzerland)
|April 26, 2025
Summary
This review compares traditional and deep learning image denoising methods. It highlights the effectiveness of deep neural networks in noise removal while preserving image details, offering insights for future research.
Area of Science:
- Image Processing
- Computer Vision
- Artificial Intelligence
Background:
- Image noise degrades quality due to acquisition and transmission.
- Effective image denoising is crucial for subsequent tasks like segmentation and recognition.
- Two-dimensional amplitude images are ubiquitous, making denoising research a priority.
Purpose of the Study:
- To provide a comprehensive overview and comparison of traditional and deep learning-based image denoising methods.
- To classify and summarize existing denoising approaches.
- To identify future research challenges and directions in image denoising.
Main Methods:
- Review and classification of classic traditional denoising techniques (e.g., BM3D).
- Analysis of deep neural network-based image denoising frameworks.
- Quantitative and qualitative comparisons using a public denoising dataset.
Main Results:
- Deep learning methods show significant promise for image denoising.
- Traditional methods like BM3D effectively remove noise while retaining details.
- Comparative analysis provides insights into the strengths and weaknesses of different approaches.
Conclusions:
- Deep learning is a key future direction for image denoising.
- Understanding algorithm differences aids in selection and innovation.
- This review offers valuable perspectives for researchers in the field.
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