Automatic Image Colorization using Ensemble of Deep Convolutional Neural Networks

Abstract

Image colorization is a technique to add color values to the grayscale image by learning the mapping between input intensity and probable chrominance values. This paper proposes an end-to-end image colorization architecture based on an ensemble of deep convolutional neural networks (DCNN). Ensemble DCNN architecture for image colorization is our novel contribution. The architecture takes inspiration from the encoder-decoder design. The encoder comprises various pre-trained DCNN models, and the decoder consists of a series of convolution and up-sampling layers. The decoder enables the merging and propagation of multi-level features from DCNN models. Further, we explore different fusion strategies to combine multi-level features from DCNNs as part of an ensemble encoder. We have experimented with DIV2k and CIFAR10 datasets. The performance of our proposed approach is evaluated in terms of subjective and reference-based image quality assessment metrics, which shows that our results are pretty competitive compared to the existing approaches.