Acquiring high-resolution 3D surface structures is a crucial task in computer vision as it
provides more detailed surface textures and clearer structures. Photometric stereo can …

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 …

Existing unsupervised Multi-View Stereo (MVS) methods generally construct supervision on
the basis of the photometric consistency loss, which suffers from unreliable supervision and …

Stereo image super-resolution (stereoSR) aims to enhance the quality of super-resolution
results by incorporating complementary information from an alternative view. Although …

Recently, parallax attention based stereo image super-resolution (SR) methods, which can
better explore cross-view information, have been widely studied. Despite the impressive …

Inspired by the great success of the Transformer in computer vision, some works have
started to explore the use of the Transformer for super-resolution (SR). However, with regard …

Images captured in challenging conditions often suffer from the co-existence of low contrast
and low resolution. However, most joint enhancement methods focus on fitting a direct …

It is very challenging to fully use cross-view information for stereo image super-resolution.
Previous methods using pixel-based parallax-attention mechanisms do not consider …

The U-Net architecture has exhibited significant efficacy across various vision tasks, yet its
adaptation for Video Super-Resolution (VSR) remains underexplored. While the Video …

Multi-image super-resolution (MISR) allows to increase the spatial resolution of a low-
resolution (LR) acquisition by combining multiple images carrying complementary …