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Published on: June 18, 2021
Fuzzy peer groups for reducing mixed gaussian-impulse noise from color images.
Samuel Morillas1, Valentín Gregori, Antonio Hervas
1Universidad Politecnica de Valencia, Department of Applied Mathematics, Valencia, Spain. smorillas@mat.upv.es
Summary
This study introduces a fuzzy peer group concept for image denoising. This novel fuzzy logic approach effectively suppresses Gaussian, impulse, and mixed noise in images.
Area of Science:
- Computer Vision
- Image Processing
- Artificial Intelligence
Background:
- Pixel similarity is key for image denoising.
- Defining pixel similarity precisely is challenging.
- Existing methods struggle with crisp similarity definitions.
Purpose of the Study:
- Introduce the fuzzy peer group concept.
- Extend the peer group concept using fuzzy logic.
- Develop a novel fuzzy logic-based image denoising filter.
Main Methods:
- Define fuzzy peer group as a fuzzy set.
- Utilize fuzzy similarity for membership degrees.
- Design a two-step filter: fuzzy switching and fuzzy averaging.
- Employ a novel fuzzy logic procedure for fuzzy peer group determination.
Main Results:
- The proposed filter efficiently suppresses Gaussian, impulse, and mixed noise.
- The fuzzy peer group concept enables computational savings.
- Experimental results demonstrate promising performance.
Conclusions:
- The fuzzy peer group concept is effective for image denoising.
- The proposed filter offers a robust solution for various noise types.
- Fuzzy logic provides a flexible framework for image processing tasks.
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