Can fully convolutional networks perform well for general image restoration problems?

We present a fully convolutional network(FCN) based approach for color image restoration. Fully convolutional networks have recently shown remarkable performance for high level vision problems like semantic segmentation. In this paper, we investigate if fully convolutional networks can show promising performance for low level problems like image restoration as well. We propose a FCN model, that learns a direct end-to-end mapping between the corrupted images as input and the desired clean images as output. Experimental results show that our FCN model outperforms traditional sparse coding based methods and demonstrates competitive performance compared to the state-of-the-art methods for image denoising. We further show that our proposed model can solve the difficult problem of blind image inpainting and can produce reconstructed images of impressive visual quality.