In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max pooling, one fully connected layer and three output nodes. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating scene illumination. This approach achieves state-of-the-art performance on a standard dataset of RAW images. Preliminary experiments on images with spatially varying illumination demonstrate the stability of the local illuminant estimation ability of our CNN. 81 978-1-4673-6759-2/15/$31.00 ©2015 IEEE
A light source estimation algorithm, which employs a CNN that contains one convolutional layer, one fully connected layer and three output nodes was proposed by Bianco et al. in REF .
Color constancy using CNNs
2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Computer Science