Technology Category
Computer Vision: Multi-modal VisionMachine Learning: Multimodal LearningIntelligent Robots: Multimodal Perception & Sensor Fusion
Multi-illuminant Color Constancy via Multi-scale Illuminant Estimation and Fusion
🤖 AI Summary
Existing color constancy methods suffer from localized chromatic distortions in multi-illuminant scenes due to single-scale illumination modeling. To address this, we propose a multi-scale illumination estimation and fusion framework. Our method employs a three-branch convolutional network to jointly extract features at multiple scales, models the illumination map as a linear combination of scale-specific illumination components, and introduces an attention-driven adaptive fusion module that dynamically weights each component for pixel-wise illumination prediction. This design enables precise, spatially varying illumination estimation tailored to complex lighting conditions. Evaluated on multiple benchmark datasets, our approach achieves state-of-the-art performance in color restoration accuracy—particularly in regions with heterogeneous illuminants—demonstrating the effectiveness of multi-scale modeling and attention-guided adaptive fusion for illumination estimation.
Technology Category
Application Category
📝 Abstract
Multi-illuminant color constancy methods aim to eliminate local color casts within an image through pixel-wise illuminant estimation. Existing methods mainly employ deep learning to establish a direct mapping between an image and its illumination map, which neglects the impact of image scales. To alleviate this problem, we represent an illuminant map as the linear combination of components estimated from multi-scale images. Furthermore, we propose a tri-branch convolution networks to estimate multi-grained illuminant distribution maps from multi-scale images. These multi-grained illuminant maps are merged adaptively with an attentional illuminant fusion module. Through comprehensive experimental analysis and evaluation, the results demonstrate the effectiveness of our method, and it has achieved state-of-the-art performance.
Problem
Research questions and friction points this paper is trying to address.
Eliminate local color casts
Improve multi-scale illuminant estimation
Enhance illuminant map accuracy
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-scale illuminant estimation
Tri-branch convolution networks
Attentional illuminant fusion module
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