Perception-Inspired Color Space Design for Photo White Balance Editing

TL;DR

This paper introduces a perception-inspired, learnable HSI color space for improved white balance correction in images, addressing limitations of traditional additive color models and demonstrating superior performance on benchmark datasets.

Contribution

The paper proposes a novel perception-inspired LHSI color space and a tailored Mamba-based network for more effective WB correction, enhancing color disentanglement and adaptability.

Findings

Outperforms existing WB correction methods on benchmark datasets

Effectively disentangles luminance and chromatic components

Demonstrates the potential of perception-inspired color spaces in computational photography

Abstract

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.