Edit Propagation Using Deep Neural Network from a Single Image

This paper proposes a novel edit propagation approach using deep neural network (DNN) from a single image, which aims to handle the problems such as appropriate features choosing and manual feature tuning. Firstly, we transform user interactions into distance maps which are then concatenated into the input image to create a new image with multiple channels, combining low-level visual features with spatial features. Secondly, we extract small multi-channel patches and use them as input of a DNN that extracts deep features adapted to user interactions. And the DNN can perform a joint end-to-end learning of visual feature and spatial feature for edit propagation, which automatically determines the importance of image features. Finally, we use the DNN as a classifier to estimate probabilities of all image pixels, and obtain editing results with high quality through further post-processing. The experimental results on MARA 1k database demonstrate that our method can respond to user interactions well and perform significantly better to propagate image edits. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.