||Deep Fusion: Illuminant Estimation using a Camera and a Spectral Sensor
||(Sungmin Woo) ; (Rayun Boo)
|| Color constancy; Illuminant estimation; Multi-spectral imaging
||In this study, we present a novel deep-fusion architecture aimed at enhancing color prediction for a light source by leveraging both spectral sensor and image. Recently, the advancements in sensor technology have led to the emergence of spectral sensors capable of capturing multi-wavelength information. While images obtained from camera sensors provide abundant pixel-level details, the information pertaining to wavelength is limited to the conventional RGB channels. Therefore, our approach introduces a deep learning framework that simultaneously integrates images, spectral information, and their combined representation. By comparing the performance of various architectures when predicting the light source color, we identify the most suitable structure and verify the helpfulness of multi-wavelength information without spatial information. To achieve this, we construct a dataset that captures both spectrum and image data concurrently, which are then utilized for conducting experiments.