Research on Image Up-scaling and Super-resolution Based on Convolutional Neural Network

Abstract

Image upsampling and super-resolution techniques are of great significance in many fields, which can improve image quality and enhance information extraction capabilities. Image upsampling and super-resolution techniques have wide applications in the real world, such as image quality enhancement, satellite and remote sensing images, and security and surveillance. This paper describes and analyzes a machine learning approach to up-scaling a low resolution images using Convolutional Neural Networks. We build on previous works on single image super-resolution. We train 3 structurally similar but different models and obtain an improvement of up to 3% compared to the more common image upscaling method, Bicubic Interpolation.