# Perception-Inspired Color Space Design for Photo White Balance Editing

arXiv: https://arxiv.org/abs/2512.09383 · HTML: https://mlsanity.com/paper/2512.09383/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 13 of 13)

- [MambaVision: A Hybrid Mamba-Transformer Vision Backbone](https://mlsanity.com/paper/2407.08083/) (2024)
- [HVI: A New Color Space for Low-light Image Enhancement](https://mlsanity.com/paper/2502.20272/) (2025)
- [Deep White-Balance Editing](https://mlsanity.com/paper/2004.01354/) (2020)
- [Convolutional Color Constancy](https://mlsanity.com/paper/1507.00410/) (2015)
- [Cross-Camera Convolutional Color Constancy](https://mlsanity.com/paper/2011.11890/) (2020)
- [On Finding Gray Pixels](https://mlsanity.com/paper/1901.03198/) (2019)
- [Auto White-Balance Correction for Mixed-Illuminant Scenes](https://mlsanity.com/paper/2109.08750/) (2021)
- [Time-Aware Auto White Balance in Mobile Photography](https://mlsanity.com/paper/2504.05623/) (2025)
- [GCC: Generative Color Constancy via Diffusing a Color Checker](https://mlsanity.com/paper/2502.17435/) (2025)
- [Modeling the Lighting in Scenes as Style for Auto White-Balance Correction](https://mlsanity.com/paper/2210.09090/) (2022)
- [Rethinking Nighttime Image Deraining via Learnable Color Space Transformation](https://mlsanity.com/paper/2510.17440/) (2025)
- [Point Cloud Color Constancy](https://mlsanity.com/paper/2111.11280/) (2021)
- [Integral Fast Fourier Color Constancy](https://mlsanity.com/paper/2502.03494/) (2025)

## Built on by (most cited first, 2 of 2)

- [Unifying Color and Lightness Correction with View-Adaptive Curve Adjustment for Robust 3D Novel View Synthesis](https://mlsanity.com/paper/2602.18322/) (2026)
- [SAFE: Scene-Aware Feature Modulation for Color Constancy with Learned Color Space in Pure-Color Scenes](https://mlsanity.com/paper/2608.13967/) (2026)

## Abstract (arXiv)

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.