*6.2.1 Image restoration*

The operation of image restoration is to recover a damaged or corrupt image for the clean image such as image denoising and super-resolution. Therefore, a natural way to implement this idea is to utilize a pre-trained encoder-decoder network, where the encoder can map a noise image into a high-level representation, and the decoder can transform the representation into an original image. For example, Mao et al. [49] apply a deep convolutional encoder-decoder network for image restoration, in particular the shortcut connection method is adopted between the encoder and decoder, which has been demonstrated in Section 3.2. And the transposed convolution is used for constructing the decoder network, as mentioned in Section 2.2. Similar work in [50] has also been introduced for image restoration, in which a residual method is used in the network (i.e., in Section 3.2).
