Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2
This work addresses image colorization for applications like historical photo restoration, but it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of colorizing grayscale images by proposing a model that combines a CNN trained from scratch with features from Inception-ResNet-v2, achieving results evaluated through a user study for public acceptance.
We review some of the most recent approaches to colorize gray-scale images using deep learning methods. Inspired by these, we propose a model which combines a deep Convolutional Neural Network trained from scratch with high-level features extracted from the Inception-ResNet-v2 pre-trained model. Thanks to its fully convolutional architecture, our encoder-decoder model can process images of any size and aspect ratio. Other than presenting the training results, we assess the "public acceptance" of the generated images by means of a user study. Finally, we present a carousel of applications on different types of images, such as historical photographs.