Fusing Convolutional Neural Networks with a Restoration Network for Increasing Accuracy and Stability

Hamed Habibi Aghdam, Elnaz Jahani Heravi, Domenec Puig

Abstract: In this paper, we propose a ConvNet for restoring images. Our ConvNet is different from state-of-art denoising networks in the sense that it is deeper and instead of restoring the image directly, it generates a pattern which is added with the noisy image for restoring the clean image. Our experiments shows that the Lipschitz constant of the proposed network is less than 1 and it is able to remove very strong as well as very slight noises. This ability is mainly because of the shortcut connection in our network. We compare the proposed network with another denoisnig ConvNet and illustrate that the network without a shortcut connection acts poorly on low magnitude noises. Moreover, we show that attaching the restoration ConvNet to a classification network increases the classification accuracy. Finally, our empirical analysis reveals that attaching a classification ConvNet with a restoration network can significantly increase its stability against noise.