DCA-LUT：基于5D查找表的深度色彩对齐紫边去除方法
DCA-LUT: Deep Chromatic Alignment with 5D LUT for Purple Fringing Removal
AI总结：
针对镜头轴向色差导致的紫边伪影问题，提出首个紫边去除深度学习框架DCA-LUT，结合CA-CT模块与5D LUT实现高效色彩校正，构建PF-Synth数据集，实验验证其性能达当前最优。
摘要：
紫边是相机镜头中轴向色差（LCA）导致的顽固性伪影，长期以来一直降低数字成像的清晰度与真实感。传统解决方案依赖复杂昂贵的复消色差（APO）镜头硬件以及手工特征提取，忽视了数据驱动方法。为填补这一空白，我们提出DCA-LUT，这是首个用于紫边去除的深度学习框架。受该问题的物理根源——镜头色散导致的RGB色彩通道空间错位启发，我们提出全新的色彩感知坐标变换（CA-CT）模块，学习图像自适应色彩空间以将紫边解耦并分离到专门维度。这种定向分离让网络能够学习精准的“紫边通道”，进而指导亮度通道的准确复原。最终色彩校正通过学习得到的5维查找表（5D LUT）完成，可实现高效且强大的非线性色彩映射。为支撑鲁棒训练与公平评估，我们构建了大规模合成紫边数据集PF-Synth。在合成数据集与真实世界数据集上开展的大量实验表明，我们的方法在紫边去除任务上取得了当前最优性能。
英文摘要：
Purple fringing, a persistent artifact caused by Longitudinal Chromatic Aberration (LCA) in camera lenses, has long degraded the clarity and realism of digital imaging. Traditional solutions rely on complex and expensive apochromatic (APO) lens hardware and the extraction of handcrafted features, ignoring the data-driven approach. To fill this gap, we introduce DCA-LUT, the first deep learning framework for purple fringing removal. Inspired by the physical root of the problem, the spatial misalignment of RGB color channels due to lens dispersion, we introduce a novel Chromatic-Aware Coordinate Transformation (CA-CT) module, learning an image-adaptive color space to decouple and isolate fringing into a dedicated dimension. This targeted separation allows the network to learn a precise ``purple fringe channel", which then guides the accurate restoration of the luminance channel. The final color correction is performed by a learned 5D Look-Up Table (5D LUT), enabling efficient and powerful% non-linear color mapping. To enable robust training and fair evaluation, we constructed a large-scale synthetic purple fringing dataset (PF-Synth). Extensive experiments in synthetic and real-world datasets demonstrate that our method achieves state-of-the-art performance in purple fringing removal.
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