Leveraging Color Naming for Image Enhancement

2026-07-09

AI Summary

This work proposes a novel interpretable image enhancement framework that addresses the common limitations of existing methods—namely, their lack of interpretability and limited support for user interaction. The approach uniquely integrates color naming with adjustable global tone curves by learning dedicated tone mappings for each named color. To enable spatially adaptive local adjustments, it further incorporates a Transformer module that effectively models contextual information across the image. The resulting method achieves state-of-the-art performance on multiple tasks, including image retouching, tone mapping, and exposure correction. Importantly, it offers an intuitive, semantically meaningful user interface that allows direct manipulation through color names, thereby balancing high performance with enhanced interpretability and user control.

Abstract

Enhancing images to make them visually appealing is a persistent challenge in computer vision. Many deep-learning methods train models on paired datasets to replicate expert editing styles. However, these approaches struggle with two key issues: (1) interpretability and (2) a parametrization suitable for user adjustments. To address these challenges, we present NamedCurves+, an approach inspired by the concept of Color Naming, a universal set of familiar colors widely used in software tools for intuitive editing. Our method integrates color names into a learning-based framework, enabling global adjustments for each named color through tone curves. To address local image variations, we incorporate a transformer block that captures spatial dependencies, enabling context-aware edits across the image. NamedCurves+ enhances the retouching process's interpretability and supports user interaction, allowing flexible modifications of individual tone curves to refine the retouched image according to personal preferences. Extensive experiments on tasks such as image retouching, tone mapping, and exposure correction demonstrate that NamedCurves+ outperforms state-of-the-art methods. Notably, our approach is both explainable, as the tone curves explicitly represent how each color name contributes to the enhancement, and interactive, allowing users to customize the retouching process and achieve results tailored to their liking.

Problem

• image enhancement
• interpretability
• user interaction
• color naming
• tone curves

Innovation

• Color Naming
• Tone Curves
• Interpretable Enhancement
• Interactive Image Editing
• Transformer-based Context Modeling