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Updated: Feb 25, 2026

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Published on: August 17, 2011
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Example-Based Image Colorization Using Locality Consistent Sparse Representation
Summary
This study introduces a novel example-based image colorization technique using locality consistent sparse representation. The method effectively colorizes grayscale images from a single reference, outperforming existing approaches.
Area of Science:
- Computer Vision
- Image Processing
- Artificial Intelligence
Background:
- Image colorization aims to add color to grayscale images, a complex task.
- Existing methods often struggle with naturalness and accuracy.
Purpose of the Study:
- To develop an automatic example-based image colorization method.
- To improve colorization accuracy and coherence using sparse representation.
Main Methods:
- A novel locality consistent sparse representation is employed.
- Features are extracted at the superpixel level (intensity, texture, semantic).
- A locality promoting regularization term enhances matching consistency.
Main Results:
- The method successfully colorizes grayscale images using a single reference.
- It outperforms state-of-the-art methods visually and quantitatively.
- An edge-preserving filter improves color coherence and sharpness.
Conclusions:
- The proposed sparse pursuit image colorization method is effective.
- It offers a robust and efficient solution for grayscale image colorization.
- This work represents a significant advancement in single-reference image colorization.
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