Image Denoising Using Very Deep Fully Convolutional Encoder Decoder Networks with Symmetric Skip Connections
Image denoising is a long standing problem in computer vision and image processing, as well as a test bed for low level image modeling algorithms. In this paper, we propose a very deep encoding decoding framework for image denoising. Instead of using image priors, the proposed framework learns end to end fully convolutional mappings from noisy images to the clean ones. The network is composed of multiple layers of convolution and de convolution operators. With the observation that deeper networks improve denoising performance, we propose to use significantly deeper networks than those employed previously for low level image processing tasks such as denoising. We propose to symmetrically link convolutional and de convolutional layers with skip layer connections, with which the training converges much faster and attains a higher quality local optimum. From the image processing point of view, those symmetric connections help preserve image details.
ArXiv