We present a fully convolutional network(FCN) based approach for color image
restoration. FCNs have recently shown remarkable performance for high-level
vision problem like semantic segmentation...In this paper, we investigate if FCN
models can show promising performance for low-level problems like image
restoration as well. We propose a fully convolutional model, that learns a
direct end-to-end mapping between the corrupted images as input and the desired
clean images as output. Our proposed method takes inspiration from domain
transformation techniques but presents a data-driven task specific approach
where filters for novel basis projection, task dependent coefficient
alterations, and image reconstruction are represented as convolutional
networks. 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.(read more)