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 …

Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …

Though many attempts have been made in blind super-resolution to restore low-resolution
images with unknown and complex degradations, they are still far from addressing general …

Both convolutional and recurrent operations are building blocks that process one local
neighborhood at a time. In this paper, we present non-local operations as a generic family of …

Recent research on super-resolution has progressed with the development of deep
convolutional neural networks (DCNN). In particular, residual learning techniques exhibit …

Despite the breakthroughs in accuracy and speed of single image super-resolution using
faster and deeper convolutional neural networks, one central problem remains largely …

Deep convolutional networks have become a popular tool for image generation and
restoration. Generally, their excellent performance is imputed to their ability to learn realistic …

Both non-local (NL) operation and sparse representation are crucial for Single Image Super-
Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non …

The aim of this paper is to propose a mechanism to efficiently and explicitly model image
hierarchies in the global, regional, and local range for image restoration. To achieve that, we …

Recently, several models based on deep neural networks have achieved great success in
terms of both reconstruction accuracy and computational performance for single image …