SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution
This work addresses the issue of blurry outputs in SISR for applications like image enhancement, though it is incremental as it builds on existing CNN and GAN approaches.
The paper tackles the problem of recovering high-frequency details in single image super-resolution (SISR) by proposing SRPGAN, a perceptual generative adversarial network framework that uses a robust perceptual loss. The results show that this method achieves higher structural similarity index (SSIM) scores on most benchmarks compared to previous state-of-the-art methods.
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.