Image Restoration Using Very Deep Convolutional Encoder ...

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Image Restoration Using very deep ConvolutionalEncoder- decoder Networks with Symmetric SkipConnectionsXiao-Jiao Mao , Chunhua Shen?, Yu-Bin Yang State Key Laboratory for Novel Software Technology, Nanjing University, China?School of Computer Science, University of Adelaide, AustraliaAbstractIn this paper, we propose a very deep fully Convolutional encoding-decoding frame-work for Image Restoration such as denoising and super-resolution. The network iscomposed of multiple layers of convolution and deconvolution operators, learningend-to-end mappings from corrupted images to the original ones. The convolu-tional layers act as the feature extractor, which capture the abstraction of imagecontents while eliminating noises/corruptions. Deconvolutional layers are thenused to recover the Image details. We propose to symmetrically link convolutionaland deconvolutional layers with skip-layer connections, with which the trainingconverges much faster and attains a higher-quality local optimum.

The proposed framework mainly contains a chain of convolutional layers and symmetric decon-volutional layers, as shown in Figure 1. We term our method “RED-Net”—very deep Residual Encoder-Decoder Networks. 2.1 Architecture The framework is fully convolutional and deconvolutional. Rectification layers are added after each

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