提出一种实时多光源白平衡算法，比神经网络更快更轻量。
Integral Fast Fourier Color Constancy
- 用积分紫外直方图加速全图区域计算，结合傅里叶并行卷积生成平滑光照图。
- 在多光源场景下精度媲美甚至超过像素级神经网络，参数量减少400倍以上。
- 适合嵌入式设备或实时视频处理，兼顾精度与速度，无需复杂训练。
传统自动白平衡（AWB）算法通常假设单一全局光源，导致多光源场景中出现色彩失真。尽管近期基于神经网络的方法在该场景表现优异，但其高参数量和计算开销限制了实时视频应用的可行性。快速傅里叶白平衡（FFCC）算法针对单光源场景设计，能高效预测全局光源。然而，它无法直接用于多光源场景，除非特别修改。为此，本文提出积分快速傅里叶白平衡（IFFCC），作为FFCC在多光源场景的扩展。IFFCC利用提出的积分紫外直方图，加速笛卡尔空间中所有可能区域的直方图计算，并并行化傅里叶基卷积操作，生成空间平滑的光照估计图。该方法在多光源场景中实现高精度、实时的自动白平衡。大量实验表明，IFFCC的精度与或优于像素级神经网络，参数量减少超过400倍，处理速度比网络方法快20至100倍。
原文摘要 · Abstract (English)
Traditional auto white balance (AWB) algorithms typically assume a single global illuminant source, which leads to color distortions in multi-illuminant scenes. While recent neural network-based methods have shown excellent accuracy in such scenarios, their high parameter count and computational demands limit their practicality for real-time video applications. The Fast Fourier Color Constancy (FFCC) algorithm was proposed for single-illuminant-source scenes, predicting a global illuminant source with high efficiency. However, it cannot be directly applied to multi-illuminant scenarios unless specifically modified. To address this, we propose Integral Fast Fourier Color Constancy (IFFCC), an extension of FFCC tailored for multi-illuminant scenes. IFFCC leverages the proposed integral UV histogram to accelerate histogram computations across all possible regions in Cartesian space and parallelizes Fourier-based convolution operations, resulting in a spatially-smooth illumination map. This approach enables high-accuracy, real-time AWB in multi-illuminant scenes. Extensive experiments show that IFFCC achieves accuracy that is on par with or surpasses that of pixel-level neural networks, while reducing the parameter count by over $400\times$ and processing speed by 20 - $100\times$ faster than network-based approaches.
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