Single image super-resolution: a comprehensive review and recent insight

Abstract

Super-resolution (SR) is a long-standing problem in image processing and computer vision and has attracted great attention from researchers over the decades. The main concept of SR is to reconstruct images from low-resolution (LR) to high-resolution (HR).It is an ongoing process in image technology, through up-sampling, de-blurring, and de-noising. Convolution neural network (CNN) has been widely used to enhance the resolution of images in recent years. Several alternative methods use deep learning to improve the progress of image super-resolution based on CNN. Here, we review the recent findings of single image super-resolution using deep learning with an emphasis on distillation knowledge used to enhance image super-resolution., it is also to highlight the potential applications of image super-resolution in security monitoring, medical diagnosis, microscopy image processing, satellite remote sensing, communication transmission, the digital multimedia industry and video enhancement. Finally, we present the challenges and assess future trends in super-resolution based on deep learning.

Graphical abstract

Keywords

super-resolution / deep learning / single-image / interpolation-based / learning-based / reconstruction-based

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