A super resolution method based on generative adversarial networks with quantum feature enhancement: Application to aerial agricultural images

Abstract

Super-resolution aims to enhance the quality of a low-resolution image to create a high-resolution one. Remarkable advances are witnessed in this field using machine learning techniques . This paper presents a super-resolution method based on generative adversarial networks (GAN) with quantum feature enhancement. The proposed framework uses a feature enhancement layer inspired by the quantum superposition principle . The layer was added to the state-of-the art super-resolution GAN (SRGAN) original model to enhance its performance. The model was trained and evaluated using two publicly available high-resolution aerial images datasets taken by an unmanned aerial vehicle . A set of statistically significant experiments are reported to show its performance. The structural similarity index metric (SSIM), t-distributed stochastic neighbor embedding (t-SNE) and peak signal-to-noise ratio (PSNR) are adopted to evaluate the performance of this proposal against SRGAN model. Results show that this proposal outperforms SRGAN in term of image reconstruction quality by 8% in similarity.