Multispectral imaging light estimation based on convolutional neural networks

In this work, we describe a convolutional neural network (CNN) to accurately predict field lighting. In the network structure, feature learning and regression are integrated into an optimization process to form a more effective model for scene illumination estimation, and we have added an attention mechanism to reinforce learning. This approach is trained with ICVL data sets and tested with Foster HSI data for better performance. The stability of the proposed neural network for local illumination estimation and the improvement of the global illumination estimation performance are verified by experiments on the spatial illumination variation images.