发表机构
Tokai University; Tokyo Metropolitan University（东海大学; 东京都立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对跨相机RGB映射的色相差异问题，提出色相分裂模型树方法，结合边界连续公式与路径混合，在Middlebury色卡数据集实验中提升了映射精度并抑制了色度间隙。
AI 中文摘要
我们提出了一种用于边界连续跨相机RGB映射的色相分裂模型树方法。跨相机RGB映射旨在生成跨相机的一致颜色表示，这些相机因传感器光谱灵敏度和图像信号处理流水线的差异，记录的RGB值存在不同。一种常见的基于色卡的补救方法是估计单个全局仿射颜色校正矩阵（CCM），但这种全局模型无法捕捉相机间特定色相的差异。为捕捉该特性，我们沿标量色相坐标递归划分源相机颜色空间，并构建模型树，每个节点存储一个仿射CCM。为拟合节点CCM，我们采用对数域误差目标。为防止硬色相分裂产生的伪轮廓，我们进一步引入边界连续公式，其中预测通过沿根到叶路径混合所有节点CCM的对数域输出获得。路径式混合权重在单纯形约束下优化，使用色卡对拟合损失和显式连续性正则化项，该正则化项定义在每个学习到的色相阈值两侧的确定性边界原型对上。我们使用Middlebury注册色卡数据集，在Canon EOS-1Ds Mark II到Canon EOS 20D的映射上进行实验。结果表明，色相分裂相比单个全局仿射CCM显著降低了对数RMSE，且所提出的带边界原型正则化的路径混合在两种照明条件和多种曝光条件下，同时提高了精度并抑制了学习到的色相阈值处的色度间隙。
英文摘要
We propose a hue-split model-tree method for boundary-continuous cross-camera RGB mapping. Cross-camera RGB mapping aims to produce consistent color representations across cameras whose recorded RGB values differ due to sensor spectral sensitivities and image-signal processing pipelines. A common chart-based remedy is to estimate a single global affine color correction matrix (CCM), but such a global model cannot capture hue-specific discrepancies between cameras. To capture that behavior, we recursively partitions the source-camera color space along a scalar hue coordinate and builds an model tree that stores an affine CCM at every node. For fitting the node CCMs, we utilize a log-domain error objective. To prevent false contours that arise from hard hue splits, we further introduce a boundary-continuous formulation in which the prediction is obtained by blending the log-domain outputs of all node CCMs along the root-to-leaf path. The path-wise blending weights are optimized under a simplex constraint using both a chart-pair fitting loss and an explicit continuity regularizer defined on deterministic boundary prototype pairs placed just on either side of each learned hue threshold. We conducted an experiment on a Canon EOS-1Ds Mark II to Canon EOS 20D mapping using the Middlebury Registered Color Checker dataset. The results show that hue splitting substantially reduces log-RMSE over a single global affine CCM and that the proposed path blending with boundary prototype regularization simultaneously improves accuracy and suppresses chromaticity gaps at the learned hue thresholds across two illuminants and multiple exposure conditions.
CommentsAccepted to APSIPA ASC 2026. 6 pages, 4 figures
