用多光谱传感器提升手机相机色彩校正精度
Leveraging Multispectral Sensors for Color Correction in Mobile Cameras
- 端到端联合使用高分辨率RGB与低分辨率多光谱数据
- 色彩误差比传统方法降低50%，效果更稳定
- 适合手机摄影、智能设备色彩优化场景
最近的快照式多光谱（MS）成像技术使紧凑、低成本的光谱传感器可用于消费级和移动设备。相比传统RGB传感器，这些系统能捕捉更丰富的光谱信息，从而提升关键成像任务，如色彩校正。然而，现有方法通常将色彩校正流程分阶段处理，常过早丢弃多光谱数据。本文提出一种统一的学习型框架，实现端到端色彩校正，并联合利用高分辨率RGB传感器与辅助的低分辨率多光谱传感器数据。该方法将完整流程整合于单一模型中，生成一致且色彩准确的结果。我们通过重构两种先进的图像到图像架构验证了框架的灵活性与通用性。为支持训练与评估，我们构建了一个专用数据集，整合并重用公开的光谱数据集，在多种RGB相机感测条件下进行渲染。大量实验表明，本方法显著提升色彩准确性与稳定性，相比仅用RGB或仅用多光谱的基线，误差减少高达50%。代码、模型与数据集见：https://lucacogo.github.io/Mobile-Spectral-CC/。
原文摘要 · Abstract (English)
Recent advances in snapshot multispectral (MS) imaging have enabled compact, low-cost spectral sensors for consumer and mobile devices. By capturing richer spectral information than conventional RGB sensors, these systems can enhance key imaging tasks, including color correction. However, most existing methods treat the color correction pipeline in separate stages, often discarding MS data early in the process. We propose a unified, learning-based framework that performs end-to-end color correction and jointly leverages data from a high-resolution RGB sensor and an auxiliary low-resolution MS sensor. Our approach integrates the full pipeline within a single model, producing coherent and color-accurate outputs. We demonstrate the flexibility and generality of our framework by refactoring two different state-of-the-art image-to-image architectures. To support training and evaluation, we construct a dedicated dataset by aggregating and repurposing publicly available spectral datasets, rendering under multiple RGB camera sensitivities. Extensive experiments show that our approach improves color accuracy and stability, reducing error by up to 50% compared to RGB-only and MS-driven baselines. Code, models and dataset available at: https://lucacogo.github.io/Mobile-Spectral-CC/.
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