利用多光谱传感器进行移动相机颜色校正
Leveraging Multispectral Sensors for Color Correction in Mobile Cameras
提出了一种端到端联合利用高分辨率RGB和低分辨率多光谱传感器进行颜色校正的框架，可复用于其他需要融合多传感器数据的底层视觉任务，如去噪、超分等。
原文依据 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.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近年来，快照式多光谱（MS）成像的进展使得紧凑、低成本的光谱传感器能够用于消费和移动设备。通过捕获比传统RGB传感器更丰富的光谱信息，这些系统可以增强关键的成像任务，包括颜色校正。然而，大多数现有方法将颜色校正流程分为多个独立阶段，往往在早期就丢弃了MS数据。我们提出了一个统一的、基于学习的框架，执行端到端的颜色校正，并联合利用来自高分辨率RGB传感器和辅助低分辨率MS传感器的数据。我们的方法将整个流程集成在单个模型中，产生连贯且颜色准确的输出。我们通过重构两种不同的最优图像到图像架构，展示了框架的灵活性和通用性。为了支持训练和评估，我们通过聚合和重新利用公开的光谱数据集，并在多种RGB相机灵敏度下进行渲染，构建了一个专用数据集。大量实验表明，我们的方法提高了颜色准确性和稳定性，与仅RGB和MS驱动的基线相比，误差降低了高达50%。代码、模型和数据集可在 https://lucacogo.github.io/Mobile-Spectral-CC/ 获取。
英文原文摘要 1292 字符
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/.