The key objective of super-resolution (SR) imaging is to reconstruct a higher-resolution
image based on a set of images, acquired from the same scene and denoted as 'low …

We present a novel method that can enhance the spatial resolution of stereo images using a
parallax prior. While traditional stereo imaging has focused on estimating depth from stereo …

Stereo super-resolution (SR) aims to enhance the spatial resolution of one camera view
using additional information from the other. Previous deep-learning-based stereo SR …

Deep learning-based methods have achieved remarkable performance in single image
super-resolution. However, these methods cannot be effectively applied in stereoscopic …

Stereo video super-resolution (SVSR) aims to enhance the spatial resolution of the low-
resolution video by reconstructing the high-resolution video. The key challenges in SVSR …

Recently, deep learning-based stereoscopic image super-resolution has attracted extensive
attention and made great progress. However, existing methods have not adequately …

The long-fascinated idea of creating 3D images that depict depth information along with
color and brightness has been realized with the advent of a light-field camera (LFC) …

Stereo image super-resolution (StereoISR) aims at recovering high resolution (HR) details
and realistic textures from low-resolution (LR) left-right pairs with stereo correspondence …

Stereo images can provide more extra information to enhance performance of super-
resolution (SR) owing to two different visual angles. Due to the attention mechanism has …

Most existing learning-based methods for stereoscopic image super-resolution rely on a
great number of high-resolution stereoscopic images as labels. To alleviate the problem of …