*2.3. Very Deep Convolutional Networks for Image Super-Resolution (VDSR)*

The ultra-deep super-resolution network proposed by Kim et al. [8] extended the SRCNN from a 3-layer shallow network structure to a 20-layer ultra-deep network, and they concluded that the reconstruction effect will be improved as the number of layers increases. Compared to SRCNN, which only depends on the image context in a small area, VDSR, by exploring more contextual information through a larger receptive field, helps to better restore the detailed structure, especially in super-resolution applications with large magnification factors. In addition, in order to solve the problem of slow convergence in SRCNN, VDSR residual learning was introduced, which greatly increased the learning rate.

VDSR accepts image features of different scales by adjusting the size of the filter to produce a fixed feature output. Although VDSR can achieve specific-scale magnification, it cannot achieve free-scale, multiscale magnification, and its parameter storage and retrieval also have obvious shortcomings.
