GAN-Based Two-Step Pipeline for Real-World Image Super-Resolution

Mostly, the prior works on single image super-resolution are dependent on high-resolution image and their bicubically downsampled low-resolution image pairs. Such methods have achieved outstanding results in single image super-resolution. Yet, these methods struggle to generalize real-world low-resolution images. Real-world low-resolution images have large varieties of degradation, and modeling these degradation accurately is a challenging task. Although some works have been proposed to address this problem, their results still lack fine perceptual details. Here, we use a GAN-based two-step pipeline to address this challenging problem of real-world image super-resolution. At first, we train a GAN network that transforms real-world low-resolution images to a space of bicubic images of the same size. This network is trained on real-world low-resolution images as input and bicubically downsampled version of their corresponding high-resolution images as ground truth. Then, we employ the nESRGAN+ network trained on bicubically downsampled low-resolution and high-resolution image pairs to super-resolve the transformed bicubic alike images. Hence, the first network transforms the wide varieties of degraded images into the bicubic space, and the second network upscales the first output by the factor of four. We show the effectiveness of this work by evaluating its output on various benchmark test datasets and comparing our results with other works. We also show that our work outperforms prior works in both qualitative and quantitative comparison. We have published our source code and trained models here for further research and improvement.