Boosting Illuminant Estimation in Deep Color Constancy through Enhancing Brightness Robustness

2025-02-18

AI Summary

Current deep neural network-based color constancy (DNNCC) models exhibit significant sensitivity to luminance variations, leading to substantial degradation in illuminant chromaticity estimation under real-world lighting conditions. This work is the first to systematically analyze the impact of luminance perturbations on DNNCC robustness and proposes BRE—a hyperparameter-free, plug-and-play framework for enhancing luminance robustness. BRE comprises two core components: adaptive-step adversarial luminance augmentation and luminance-aware contrastive optimization—both requiring no architectural modifications to the backbone network and incurring zero additional inference overhead. Evaluated on ColorChecker and Cube+ benchmarks, BRE consistently improves six state-of-the-art DNNCC models, reducing their average illuminant estimation error by 5.04%. Crucially, this performance gain is achieved without any computational cost during inference. Our approach establishes a novel paradigm for improving the generalization capability of color constancy models under complex, varying illumination conditions.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Color constancy estimates illuminant chromaticity to correct color-biased images. Recently, Deep Neural Network-driven Color Constancy (DNNCC) models have made substantial advancements. Nevertheless, the potential risks in DNNCC due to the vulnerability of deep neural networks have not yet been explored. In this paper, we conduct the first investigation into the impact of a key factor in color constancy-brightness-on DNNCC from a robustness perspective. Our evaluation reveals that several mainstream DNNCC models exhibit high sensitivity to brightness despite their focus on chromaticity estimation. This sheds light on a potential limitation of existing DNNCC models: their sensitivity to brightness may hinder performance given the widespread brightness variations in real-world datasets. From the insights of our analysis, we propose a simple yet effective brightness robustness enhancement strategy for DNNCC models, termed BRE. The core of BRE is built upon the adaptive step-size adversarial brightness augmentation technique, which identifies high-risk brightness variation and generates augmented images via explicit brightness adjustment. Subsequently, BRE develops a brightness-robustness-aware model optimization strategy that integrates adversarial brightness training and brightness contrastive loss, significantly bolstering the brightness robustness of DNNCC models. BRE is hyperparameter-free and can be integrated into existing DNNCC models, without incurring additional overhead during the testing phase. Experiments on two public color constancy datasets-ColorChecker and Cube+-demonstrate that the proposed BRE consistently enhances the illuminant estimation performance of existing DNNCC models, reducing the estimation error by an average of 5.04% across six mainstream DNNCC models, underscoring the critical role of enhancing brightness robustness in these models.

Problem

• Enhance brightness robustness in color constancy.
• Address DNN sensitivity to brightness variations.
• Improve illuminant estimation in deep learning models.

Innovation

• Enhances brightness robustness
• Adopts adversarial brightness augmentation
• Integrates brightness-robustness-aware optimization