Abstract Single Image Super Resolution (SISR) is a process to obtain a high pixel density
and refined details from a low resolution (LR) image to get upscaled and sharper high …

We study on image super-resolution (SR), which aims to recover realistic textures from a low-
resolution (LR) image. Recent progress has been made by taking high-resolution images as …

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

Single image super-resolution is the task of inferring a high-resolution image from a single
low-resolution input. Traditionally, the performance of algorithms for this task is measured …

Due to the significant information loss in low-resolution (LR) images, it has become
extremely challenging to further advance the state-of-the-art of single image super …

We consider how image super-resolution (SR) can contribute to an object detection task in
low-resolution images. Intuitively, SR gives a positive impact on the object detection task …

Remote sensing image super-resolution (SR) plays an important role by supplementing the
lack of original high-resolution (HR) images in the study scenarios of large spatial areas or …

Although recent years have witnessed the great advances in stereo image super-resolution
(SR), the beneficial information provided by binocular systems has not been fully used …

In this paper, we propose a novel and efficient reference feature extraction module referred
to as the Similarity Search and Extraction Network (SSEN) for reference-based super …

We present a novel approach to reference-based super-resolution (RefSR) with the focus on
dual-camera super-resolution (DCSR), which utilizes reference images for high-quality and …