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Leveraging Color Naming for Image Enhancement
Summary
NamedCurves+ uses color names and tone curves for intuitive image enhancement. This explainable and interactive approach improves image retouching, tone mapping, and exposure correction.
Area of Science:
- Computer Vision
- Image Processing
- Machine Learning
Background:
- Traditional deep learning methods for image enhancement often lack interpretability and user adjustability.
- Paired datasets used in training expert editing styles limit flexibility and understanding of the enhancement process.
Purpose of the Study:
- To develop an interpretable and user-adjustable framework for image enhancement.
- To integrate intuitive color naming concepts into a learning-based image retouching system.
Main Methods:
- NamedCurves+ utilizes color names to control global adjustments via tone curves.
- A transformer block is incorporated to capture spatial dependencies for context-aware local edits.
- The framework combines global color adjustments with local spatial context awareness.
Main Results:
- NamedCurves+ demonstrates superior performance over state-of-the-art methods in image retouching, tone mapping, and exposure correction.
- The approach provides explainable results by visualizing the contribution of each color name.
- Experiments confirm the method's effectiveness and user-friendly interactive customization.
Conclusions:
- NamedCurves+ offers an interpretable and interactive solution for image enhancement challenges.
- The integration of color naming principles enhances user control and understanding in image editing.
- This method advances computer vision by providing a flexible and explainable image enhancement tool.
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