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Published on: June 18, 2021
Self-similarity driven color demosaicking.
Antoni Buades1, Bartomeu Coll, Jean-Michel Morel
1Université Paris Descartes, 75270 Paris cedex 06, France. toni.buades@math-info.univ-paris5.fr
Summary
This study introduces a novel demosaicking method that uses image self-similarity to prevent artifacts like zipper effects and color spots. This approach improves color inference in challenging image structures.
Area of Science:
- Digital Image Processing
- Computer Vision
Background:
- Demosaicking reconstructs full color information from single-color pixels.
- Current methods struggle with thin structures, causing artifacts like zipper effects and color spots.
- Local image geometry is crucial for accurate color inference.
Purpose of the Study:
- To develop a demosaicking method that overcomes limitations of current algorithms.
- To reduce artifacts in demosaicked images, particularly in challenging structures.
- To leverage image self-similarity for improved color inference.
Main Methods:
- Involving image self-similarity to infer missing color data.
- Developing algorithms to analyze and utilize local image geometry.
- Testing the method on classic image databases.
Main Results:
- The proposed method effectively avoids artifacts like zipper effect, blur, and color spots.
- Satisfactory color inference is achieved even in critical cases with fine patterns.
- The method demonstrates robustness across different image types.
Conclusions:
- Image self-similarity is a viable approach to enhance demosaicking.
- The new method offers a significant improvement over state-of-the-art demosaicking algorithms.
- This technique provides a more accurate and artifact-free solution for color reconstruction.
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