A Single Image Super-Resolution Reconstruction Based on Fusion

Image super-resolution is to restore a high-resolution image from a low-resolution image or image sequence. High resolution means that the image has a high pixel density and can provide more details, which often play a key role in the application. Aiming at the application of single-frame low-resolution reconstruction and super-resolution, this paper proposes a method based on image fusion. This method combines two or more methods of super-resolution image reconstruction using generative adversarial neural networks. The reconstructed images are fused. Image fusion uses the integration of two or more images into a new image. Fusion can make use of the temporal and spatial correlation and information complementarity of two or more images, which can make the image obtained after fusion have a more comprehensive and clear description of the scene, which is more conducive to human eye recognition. This paper draws on the idea of ensemble learning, and uses the super-resolution images generated by the three super-resolution reconstruction algorithms of BasicSR, SRGAN and ESRGAN to carry out two-by-two cross fusion for simulation experiments. The experimental results show that this kind of reconstruction using different generation adversarial networks to generate the super-resolution image by fusion is simple and effective. The super-resolution image quality after fusion is generally better than the image quality before fusion in terms of PSNR and SSIM.