用感知启发的可学习色空间提升照片白平衡编辑效果
Perception-Inspired Color Space Design for Photo White Balance Editing
- 提出感知启发的可学习HSI色空间，解耦亮度与色彩分量
- 在基准数据集上优于传统sRGB方法，显著改善复杂光照下白平衡
- 适合计算摄影、图像处理方向研究者参考
白平衡是图像信号处理器（ISP）流水线中的关键步骤，用于消除不同光照条件引起的颜色偏差并还原场景真实色彩。当前，当原始相机RAW数据不可用时，广泛采用基于sRGB的白平衡编辑进行ISP后处理修正。然而，加性色彩模型（如sRGB）受固定非线性变换和纠缠色彩通道的固有局限，在复杂光照条件下泛化能力不足。为此，我们提出一种新型白平衡修正框架，利用感知启发的可学习HSI（LHSI）色彩空间。该空间基于柱面模型，自然分离亮度与色度成分，并引入专用参数增强解耦，结合可学习映射自适应提升灵活性。同时设计了针对LHSI特性的新Mamba网络。在基准数据集上的实验结果表明，本方法性能优越，凸显了感知启发式色彩空间设计在计算摄影中的潜力。源代码已开源：https://github.com/YangCheng58/WB_Color_Space。
原文摘要 · Abstract (English)
White balance (WB) is a key step in the image signal processor (ISP) pipeline that mitigates color casts caused by varying illumination and restores the scene's true colors. Currently, sRGB-based WB editing for post-ISP WB correction is widely used to address color constancy failures in the ISP pipeline when the original camera RAW is unavailable. However, additive color models (e.g., sRGB) are inherently limited by fixed nonlinear transformations and entangled color channels, which often impede their generalization to complex lighting conditions. To address these challenges, we propose a novel framework for WB correction that leverages a perception-inspired Learnable HSI (LHSI) color space. Built upon a cylindrical color model that naturally separates luminance from chromatic components, our framework further introduces dedicated parameters to enhance this disentanglement and learnable mapping to adaptively refine the flexibility. Moreover, a new Mamba-based network is introduced, which is tailored to the characteristics of the proposed LHSI color space. Experimental results on benchmark datasets demonstrate the superiority of our method, highlighting the potential of perception-inspired color space design in computational photography. The source code is available at https://github.com/YangCheng58/WB_Color_Space.
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