利用颜色命名进行图像增强
Leveraging Color Naming for Image Enhancement
AI总结：
针对图像增强中深度学习方法的问题，提出NamedCurves+，将颜色命名融入学习框架，通过色调曲线全局调整颜色，结合变压器模块处理局部变化，实验证明其优于现有方法，兼具可解释性与交互性。
摘要：
在计算机视觉中，增强图像以使其视觉上更吸引人是一个长期存在的挑战。许多深度学习方法在配对数据集上训练模型以复制专家编辑风格，但存在可解释性和适合用户调整的参数化这两个关键问题。为应对这些挑战，我们提出NamedCurves+，它受颜色命名概念启发，将颜色名称集成到基于学习的框架中，通过色调曲线对每个命名颜色进行全局调整，还纳入变压器模块处理局部图像变化。实验表明NamedCurves+优于现有方法，具有可解释性和交互性。
英文摘要：
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.
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