  Single image super resolution (SISR) is to reconstruct a high resolution
image from a single low resolution image. The SISR task has been a very
attractive research topic over the last two decades. In recent years,
convolutional neural network (CNN) based models have achieved great performance
on SISR task. Despite the breakthroughs achieved by using CNN models, there are
still some problems remaining unsolved, such as how to recover high frequency
details of high resolution images. Previous CNN based models always use a pixel
wise loss, such as l2 loss. Although the high resolution images constructed by
these models have high peak signal-to-noise ratio (PSNR), they often tend to be
blurry and lack high-frequency details, especially at a large scaling factor.
In this paper, we build a super resolution perceptual generative adversarial
network (SRPGAN) framework for SISR tasks. In the framework, we propose a
robust perceptual loss based on the discriminator of the built SRPGAN model. We
use the Charbonnier loss function to build the content loss and combine it with
the proposed perceptual loss and the adversarial loss. Compared with other
state-of-the-art methods, our method has demonstrated great ability to
construct images with sharp edges and rich details. We also evaluate our method
on different benchmarks and compare it with previous CNN based methods. The
results show that our method can achieve much higher structural similarity
index (SSIM) scores on most of the benchmarks than the previous state-of-art
methods.
