Purple flare, a diffuse chromatic aberration artifact commonly found around highlight areas, severely degrades the tone transition and color of the image. Existing traditional methods are based on hand-crafted features, which lack flexibility and rely entirely on fixed priors, while the scarcity of paired training data critically hampers deep learning. To address this issue, we propose a novel network built upon decoupled HSV Look-Up Tables (LUTs). The method aims to simplify color correction by adjusting the Hue (H), Saturation (S), and Value (V) components independently. This approach resolves the inherent color coupling problems in traditional methods. Our model adopts a two-stage architecture: First, a Chroma-Aware Spectral Tokenizer (CAST) converts the input image from RGB space to HSV space and independently encodes the Hue (H) and Value (V) channels into a set of semantic tokens describing the Purple flare status; second, the HSV-LUT module takes these tokens as input and dynamically generates independent correction curves (1D-LUTs) for the three channels H, S, and V. To effectively train and validate our model, we built the first large-scale purple flare dataset with diverse scenes. We also proposed new metrics and a loss function specifically designed for this task. Extensive experiments demonstrate that our model not only significantly outperforms existing methods in visual effects but also achieves state-of-the-art performance on all quantitative metrics.

翻译：紫边是一种常见于高光区域的弥散色差伪影，会严重破坏图像的色调过渡与色彩表现。现有传统方法基于手工特征设计，缺乏灵活性且完全依赖固定先验，而配对训练数据的稀缺性严重制约了深度学习方法的进展。为解决这一问题，我们提出了一种基于解耦HSV查找表的新型网络。该方法旨在通过独立调整色相、饱和度与明度分量来简化色彩校正过程，从而解决传统方法中固有的色彩耦合问题。我们的模型采用两阶段架构：首先，色度感知光谱分词器将输入图像从RGB空间转换至HSV空间，并独立地将色相通道与明度通道编码为一组描述紫边状态的语义分词；随后，HSV-LUT模块以这些分词作为输入，动态生成针对H、S、V三个通道的独立校正曲线。为有效训练与验证模型，我们构建了首个涵盖多样化场景的大规模紫边数据集，并提出了专门针对此任务设计的评估指标与损失函数。大量实验表明，我们的模型不仅在视觉效果上显著优于现有方法，同时在所有定量指标上均达到了最先进的性能水平。