Color constancy using AlexNet convolutional neural network

Abstract

Color constancy usually refers to the adaptive ability of people to correctly perceive the color of objects under any light source and is an important prerequisite for advanced tasks such as recognition, segmentation and 3D vision. The purpose of color constancy calculation is to estimate the illumination color of the image. In this work, we established the Alexnet network model to accurately estimate the lighting in the scene. The AlexNet model includes an input layer, 8 convolutional layers, AlexNet takes a 512x512 3-channel image patch as input. Compared with the previous network models, the AlexNet model contains several relatively new technical points. For the first time, ReLU, Dropout have been successfully applied in CNN. At the same time, AlexNet also uses GPU for computing acceleration. The illumination color estimation is more robust and stable, and can be combined with the field of color correction of image processing and computer vision.