Image restoration, including image denoising, super resolution, inpainting, and so on, is a well-studied problem in computer vision and image processing, as well as a test bed for low-level image modeling algorithms. In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric co-v-l-tional-deconvolutional layers. In other words, the network is composed of multiple layers of convolution and de-convolution operators, learning end-to-end mappings from corrupted images to the original ones. The convolutional layers capture the abstraction of image contents while eliminating corruptions. Deconvolutional layers have the capability to upsample the feature maps and recover the image details. To deal with the problem that deeper networks tend to be more difficult to train, we propose to symmetrically link convolutional and deconvolutional layers with skip-layer connections, with which the training converges much faster and attains better results. The skip connections from convolutional layers to their mirrored corresponding deconvolutional layers exhibit two main advantages. First, they allow the signal to be back-propagated to bottom layers directly, and thus tackles the problem of gradient vanishing, making training deep networks easier and achieving restoration performance gains consequently. Second, these skip connections pass image details from convolutional layers to deconvolutional layers, which is beneficial in recovering the clean image. Significantly, with the large capacity, we show it is possible to cope with different levels of corruptions using a single model. Using the same framework, we train models on tasks of image denoising, super resolution removing JPEG compression artifacts, and image inpainting. Our experiment results on benchmark datasets show that our network can achieve best reported performance on all of the four tasks, and set new state-of-the-art.
Mao et al. REF proposed to use skip-layer connection to symmetrically link convolutional and deconvolutional layers, which is able to train even deeper CNN architecture for denoising.
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
ArXiv
ArXiv
Computer Science