Integral Fast Fourier Color Constancy
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915141279285248 |
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| author | Wei, Wenjun Qian, Yanlin Chen, Huaian Dai, Junkang Jin, Yi |
| author_facet | Wei, Wenjun Qian, Yanlin Chen, Huaian Dai, Junkang Jin, Yi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_03494 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integral Fast Fourier Color Constancy Wei, Wenjun Qian, Yanlin Chen, Huaian Dai, Junkang Jin, Yi Image and Video Processing 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. |
| title | Integral Fast Fourier Color Constancy |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2502.03494 |