High-resolution dense prediction enables many appealing real-world applications, such as
computational photography, autonomous driving, etc. However, the vast computational cost …

Tasks that involve high-resolution dense prediction require a modeling of both local and
global patterns in a large input field. Although the local and global structures often depend …

In this work, we present QuickSRNet, an efficient super-resolution architecture for real-time
applications on mobile platforms. Super-resolution clarifies, sharpens, and upscales an …

High resolution and advanced semantic representation are both vital for dense prediction.
Empirically, low-resolution feature maps often achieve stronger semantic representation …

Super-resolution (SR) has achieved great success due to the development of deep
convolutional neural networks (CNNs). However, as the depth and width of the networks …

Depth super-resolution (DSR) aims to restore high-resolution (HR) depth from low-resolution
(LR) one, where RGB image is often used to promote this task. Recent image guided DSR …

For real applications, existing HSI-SR methods are mostly not only limited to unstable
performance under unknown scenarios but also suffer from high computation consumption …

Fundamentally, super-resolution is ill-posed problem because a low-resolution image can
be obtained from many high-resolution images. Recent studies for super-resolution cannot …

Recent advancements in deep neural networks have made remarkable leap-forwards in
dense image prediction. However, the issue of feature alignment remains as neglected by …

We propose a real-time and lightweight single-image super-resolution (SR) network named
Bicubic++. Despite using spatial dimensions of the input image across the whole network …