Super resolution (SR) is an eminent system in the field of computer vison and image
processing to improve the visual perception of the poor-quality images. The key objective of …

This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …

Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR)
image from a low-resolution (LR) observation, has been an active research topic in the area …

Recently, very deep convolutional neural networks (CNNs) have shown great power in
single image super-resolution (SISR) and achieved significant improvements against …

Recently, deep convolutional neural networks (CNNs) have been widely explored in single
image super-resolution (SISR) and obtained remarkable performance. However, most of the …

Recent advances in image super-resolution (SR) explored the power of deep learning to
achieve a better reconstruction performance. However, the feedback mechanism, which …

Recent studies have shown that deep networks can achieve promising results for image
denoising. However, how to simultaneously incorporate the valuable achievements of …

Deep convolution-based single image super-resolution (SISR) networks embrace the
benefits of learning from large-scale external image resources for local recovery, yet most …

In this paper, we propose dense feature fusion (DFF) for image super-resolution (SR). As the
same content in different natural images often have various scales and angles of view …

Abstract Generative Adversarial Networks (GAN) have demonstrated the potential to recover
realistic details for single image super-resolution (SISR). To further improve the visual …