用时间位置信息提升手机自动白平衡准确度
Time-Aware Auto White Balance in Mobile Photography
- 结合拍摄时间、位置等上下文信息优化白平衡估计
- 仅5千参数模型性能媲美甚至超越大型模型
- 新数据集含3224张带真实光照标签的照片，适合评估
相机依赖自动白平衡（AWB）校正场景光照和传感器光谱响应造成的颜色偏移。传统方法仅基于原始图像的颜色信息估计光源。移动设备还提供拍摄时间、地理位置等上下文元数据，可有效缩小可能的光照范围。本文提出一种轻量级光源估计方法，将上下文元数据、拍摄信息与图像颜色融合进一个约5000参数的小模型，表现优异，达到或超过更大模型水平。为验证方法，我们构建了包含3224张智能手机图像的数据集，覆盖不同时段与多种光照条件，每张图配有通过色卡确定的真实光源色温及用户偏好光源，构成全面的AWB评估基准。
原文摘要 · Abstract (English)
Cameras rely on auto white balance (AWB) to correct undesirable color casts caused by scene illumination and the camera's spectral sensitivity. This is typically achieved using an illuminant estimator that determines the global color cast solely from the color information in the camera's raw sensor image. Mobile devices provide valuable additional metadata-such as capture timestamp and geolocation-that offers strong contextual clues to help narrow down the possible illumination solutions. This paper proposes a lightweight illuminant estimation method that incorporates such contextual metadata, along with additional capture information and image colors, into a compact model (~5K parameters), achieving promising results, matching or surpassing larger models. To validate our method, we introduce a dataset of 3,224 smartphone images with contextual metadata collected at various times of day and under diverse lighting conditions. The dataset includes ground-truth illuminant colors, determined using a color chart, and user-preferred illuminants validated through a user study, providing a comprehensive benchmark for AWB evaluation.
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