Single image super-resolution (SISR) is the task of inferring a
high-resolution image from a single low-resolution image. Recent research on
super-resolution has achieved great progress due to the development of deep
convolutional neural networks in the field of computer vision. Existing
super-resolution reconstruction methods have high performances in the criterion
of Mean Square Error (MSE) but most methods fail to reconstruct an image with
shape edges. To solve this problem, the mixed gradient error, which is composed
by MSE and a weighted mean gradient error, is proposed in this work and applied
to a modified U-net network as the loss function. The modified U-net removes
all batch normalization layers and one of the convolution layers in each block.
The operation reduces the number of parameters, and therefore accelerates the
reconstruction. Compared with the existing image super-resolution algorithms,
the proposed reconstruction method has better performance and time consumption.
The experiments demonstrate that modified U-net network architecture with mixed
gradient loss yields high-level results on three image datasets: SET14, BSD300,
ICDAR2003. Code is available online.

本文提出了一种基于 Mixed Gradient Error 的深度学习网络 ——Modified U-net，将其作为损失函数，减少网络参数，从而加速图像重构，与现有方法相比，该重构方法具有更好的性能和时间消耗。