Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections

TL;DR

This paper introduces a very deep convolutional encoder-decoder network with symmetric skip connections for image restoration tasks like denoising and super-resolution, improving training speed and restoration quality.

Contribution

The paper presents a novel deep network architecture with symmetric skip connections that enhance training efficiency and image restoration performance.

Findings

Outperforms previous state-of-the-art methods in image denoising and super-resolution.

Faster convergence and higher-quality results due to skip-layer connections.

Handles various noise levels with a single model.

Abstract

In this paper, we propose a very deep fully convolutional encoding-decoding framework for image restoration such as denoising and super-resolution. 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 act as the feature extractor, which capture the abstraction of image contents while eliminating noises/corruptions. De-convolutional layers are then used to recover the image details. 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. First, The skip connections 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 de-convolutional layers, which is beneficial in recovering the original image. Significantly, with the large capacity, we can handle different levels of noises using a single model. Experimental results show that our network achieves better performance than all previously reported state-of-the-art methods.