Spectrum Aware Illumination Estimation Using Multispectral Image

2026-06-12

AI Summary

This work addresses the challenge that existing methods struggle to effectively exploit spectral information in multispectral images under diverse illumination and cross-sensor conditions, leading to limited performance in illuminant spectral estimation. To overcome this, we propose a novel deep learning framework that integrates illumination priors through a spatial-spectral feature extraction module coupled with a spectral attention mechanism to enhance responses in critical spectral channels. Additionally, we introduce a training-free spectral-domain transformation strategy that enables efficient transfer of illuminant spectra from high-dimensional multispectral sensors to low-dimensional camera sensors. Experiments on a newly constructed real-world multispectral dataset demonstrate that our method significantly outperforms state-of-the-art approaches, achieving highly accurate and robust illuminant spectral estimation.

Abstract

Multispectral (MS) imaging extends beyond conventional RGB imaging by capturing more spectral bands, thereby improving illuminant spectrum estimation (ISE). However, existing methods often fail to fully exploit spectral information, resulting in suboptimal performance under diverse lighting conditions and across different sensor domains. Hence, we propose a deep learning framework with a spatio-spectral feature extraction block, which incorporates spectral attention mechanisms to enhance spectral correlation and preserve illuminant-relevant spatial features. Through the inclusion of an illuminant prior (IP), our approach prioritizes specific channels that provide more meaningful information in an MS image. We also propose a spectral-domain transform across different MS sensor spaces. The results demonstrate that illuminant spectra learned in high-dimensional sensor spaces can be effectively transformed to various lower-dimensional camera sensor spaces without any additional training. To facilitate evaluation, we introduce a real-world MS dataset containing high-dimensional ground-truth illumination spectra captured under diverse lighting conditions. Through extensive experiments, we demonstrate that our method achieves superior accuracy compared to existing models, thus providing a practical solution for real-world ISE. The code and dataset are available at https://github.com/hyejin5/Spectrum-Aware-Illumination-Estimation-Using-Multispectral-Image.

Problem

• Illuminant Spectrum Estimation
• Multispectral Imaging
• Spectral Information
• Sensor Domain
• Lighting Conditions

Innovation

• multispectral imaging
• illuminant spectrum estimation
• spectral attention
• illuminant prior
• cross-sensor spectral transform