Low-rank image completion with entropy features

In this paper, we propose a novel method to complete the images or textures with the property of low rank. Our method leverages saliency detection with two entropy features to estimate initial corrupted regions. Then an iterative optimization model for low-rank and sparse errors recovery is designed to complete the corrupted images. Our iterative model can improve the initial corrupted regions and generate accurate and continuous corrupted regions via fully connected CRFs. By introducing a F-norm term in our model to absorb small noise, we can generate completed images which are more precise and have lower rank. Experiments indicate that our method introduces less local distortions than example-based methods for images with regular structures. It is also superior to the previous low-rank image completion method especially when the images contain low-rank corrupted regions. Furthermore, we show that the entropy features benefit the existing saliency detection methods too.