*2.1. SRCNN*/*Fast SRCNN (FSRCNN)*

Dong et al. [5] first proposed the use of a convolutional neural network for image super-resolution reconstruction. An LR image was first enlarged to the target size using Bicubic interpolation, and then a nonlinear mapping was performed through a three-layer convolutional network. The obtained results were output as high-resolution images, and good results were obtained. As shown in Figure 1, the network structure design of this method is simple. Compared with previous learning algorithms, it saves a lot of artificial feature extraction steps and post-integration, thus opening up the era of deep CNN super-resolution image processing problems.

**Figure 1.** Network architecture of the super-resolution convolution neural network (SRCNN).

After that, Dong et al. [6] improved upon the SRCNN, and the fast SRCNN (FSRCNN) was proposed, which increases the depth of the network and introduces a deconvolution layer to restore features. The deconvolution layer can realize the conversion from low-resolution space to high-resolution space. This feature allows the FSRCNN to directly use the low-resolution image instead of the interpolation result as the network input. Directly using LR images as input can not only reduce the calculation amount of the model, but also avoid the obvious artificial traces introduced by interpolation. FSRCNN offers a great improvement in speed, without any preprocessing, to achieve end-to-end input and output of the network, but the restoration accuracy is somewhat insufficient.
