Automatic colorization of natural images using deep learning

Abstract

An approach based on deep learning for automatic colorization of image with optional userguided hints. The system maps a grey-scale image, along with, user hints” (selected colors) to an output colorization with a Convolution Neural Network (CNN). Previous approaches have relied heavily on user input which results in non-real-time desaturated outputs. The network takes user edits by fusing low-level information of source with high-level information, learned from large-scale data. Some networks are trained on a large data set to eliminate this dependency. The image colorization systems find their applications in astronomical photography, CCTV footage, electron microscopy, etc. The various approaches combine color data from large data sets and user inputs provide a model for accurate and efficient colorization of grey-scale images. Keywords—image colorization; deep learning; convolutional neural network; image processing.