Multi-spectral illumination estimation based on residual network

Fengqi Zhao; Long Ma

doi:10.1117/12.3033887

5 July 2024 Multi-spectral illumination estimation based on residual network

Fengqi Zhao, Long Ma

Proceedings Volume 13183, International Conference on Optoelectronic Information and Functional Materials (OIFM 2024); 1318318 (2024) https://doi.org/10.1117/12.3033887
Event: The 3rd International Conference on Optoelectronic Information and Functional Materials (OIFM 2024), 2024, Wuhan, China

Abstract

In this work, a deep learning network is described that uses a residual network as the backbone and incorporates SE-Blocks to accurately predict illumination. Multispectral images are segmented into small blocks as input to estimate global illumination from local estimates. The network is composed of multiple basic residual blocks and bottleneck residual blocks, integrating feature learning and regression into the optimization process, thereby producing a more effective illumination estimation model. The entire network is trained using the ICVL dataset and tested on the Foster 2022 dataset. Preliminary experiments on images under different lighting conditions have validated the stability of the proposed neural network method for illumination estimation, enhancing the performance of illumination estimation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fengqi Zhao and Long Ma "Multi-spectral illumination estimation based on residual network", Proc. SPIE 13183, International Conference on Optoelectronic Information and Functional Materials (OIFM 2024), 1318318 (5 July 2024); https://doi.org/10.1117/12.3033887

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