In this paper, we summarize the 2nd NTIRE challenge on stereo image super-resolution
(SR) with a focus on new solutions and results. The task of the challenge is to super-resolve …

Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution
(SR), but is highly challenging due to its non-local property caused by the disparities among …

Stereo image super-resolution exploits additional features from cross view image pairs for
high resolution (HR) image reconstruction. Recently, several new methods have been …

Real-time performance of stereo matching networks is important for many applications, such
as automatic driving, robot navigation and augmented reality (AR). Although significant …

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 …

Weakly supervised learning can help local feature methods to overcome the obstacle of
acquiring a large-scale dataset with densely labeled correspondences. However, since …

With the advances of deep learning, many recent CNN-based methods have yielded
promising results for image classification. In very high-resolution (VHR) remote sensing …

Stereo matching is a fundamental building block for many vision and robotics applications.
An informative and concise cost volume representation is vital for stereo matching of high …

Single-image super-resolution (SISR) is an important task in image processing, which aims
to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and …

Under stereo settings, the problem of image super-resolution (SR) and disparity estimation
are interrelated that the result of each problem could help to solve the other. The effective …