A Novel Image Super-Resolution Reconstruction Framework Using the AI Technique of Dual Generator Generative Adversarial Network (GAN)

Abstract

Image superresolution (SR) is the process of enlarging and enhancing a low-resolution image. Image superresolution helps in industrial image enhancement, classification, detection, pattern recognition, surveillance, satellite imaging, medical diagnosis, image analytics, etc. It is of utmost importance to keep the features of the low-resolution image intact while enlarging and enhancing it. In this research paper, a framework is proposed that works in three phases and generates superresolution images while keeping low-resolution image features intact and reducing image blurring and artifacts. In the first phase, image enlargement is done, which enlarges the low-resolution image to the 2x/4x scale using two standard algorithms. The second phase enhances the image using an AI-empowered Generative adversarial network (GAN). We have used a GAN with dual generators and named it EffN-GAN (EfficientNet-GAN). Fusion is done in the last phase, wherein the final improved image is generated by fusing the enlarged image and GAN output image. The fusion phase helps in reducing the artifacts. We have used the DIV2K dataset to train the GAN and further tested the results on the images of Set5, Set14, B100, Urban100, Manga109 datasets with ground truth of size 224x224x3. The obtained results were compared with the state-of-the-art superresolution approach based on important image quality parameters, namely, Peak signal-to--to-noise ratio (PSNR), Structural similarity index (SSIM), Visual information fidelity (VIF) image quality parameters. The results show that the proposed framework for generating super-resolution images from 2x/4x resolution downgraded images improves the aforementioned mentioned image quality parameters significantly.