Multispectral Illumination Estimation Using Deep Unrolling Network


As an intrinsic physical property of materials, spectral reflectance is a rich information source for a wide range of vision tasks, including object recognition and material reproduction, as well as man technical and scientific imaging problems. However, the acquisition of accurate spectral reflectance images requires an extra per-image calibration to compensate for the illumination conditions in the scene, for example with a known reference [21] or a dedicated measurement device [2]. Unfortunately this calibration is generally cumbersome and frequently fails in complex lighting situations with multiple, different illumination sources.

Published on September 21, 2021 by Y. Li, Q. Fu, and W. Heidrich

illumination spectra estimation multispectral images technology

2 min READ

This paper examines the problem of illumination spectra estimation in multispectral images. We cast the problem into a constrained matrix factorization problem and present a method for both single-global and multiple illumination estimation in which a deep unrolling network is constructed from the alternating direction method of multipliers(ADMM) optimization for solving the matrix factorization problem. To alleviate the lack of multispectral training data, we build a large multispectral reflectance image dataset for generating synthesized data and use them for training and evaluating our model. The results of simulations and real experiments demonstrate that the proposed method is able to outperform state-of-the-art spectral illumination estimation methods, and that it generalizes well to a wide variety of scenes and spectra. PDF

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