Studies Advanced in Image Super-resolution Reconstruction based on Deep Learning

Abstract

Image super-resolution reconstruction (SR) has always been a research hotspot in the computer vision community, whose purpose is to convert low-resolution (LR) images into high-resolution (HR) images by algorithms without changing the hardware. Early SR works were mainly based on manual features and mathematical statistical models, relying on interpolation to achieve high-resolution image reconstruction. Thanks to the rapid development of convolutional neural networks, SR based on deep learning has achieved breakthroughs in both accuracy and speed. This paper first introduces the basic principles of image super-resolution reconstruction, including the basic framework and loss function of SR. Then, we introduce the representative super-resolution reconstruction methods such as SRCNN, SRGAN, and TTSR. We also introduce the common public datasets and evaluation indexes, and compare the performance of several SR methods to analyze their advantages and disadvantages. Finally, we summarize the existing challenges in the image super-resolution reconstruction, and give a look out for the future development direction of SR.