Abstract Super-resolution (SR), aiming to super-resolve degraded low-resolution image to
recover the corresponding high-resolution counterpart, is an important and challenging task …

We present a novel high-fidelity generative adversarial network (GAN) inversion framework
that enables attribute editing with image-specific details well-preserved (eg, background …

Due to the difficulty in collecting large-scale and perfectly aligned paired training data for
Under-Display Camera (UDC) image restoration, previous methods resort to monitor-based …

Deep learning has opened up new possibilities for light field super-resolution (SR), but
existing methods trained on synthetic datasets with simple degradations (eg, bicubic …

It is widely agreed that reference-based super-resolution (RefSR) achieves superior results
by referring to similar high quality images, compared to single image super-resolution …

Reference-based super-resolution (RefSR) has achieved impressive progress on the
recovery of high-frequency details thanks to an additional reference high-resolution (HR) …

The asymmetric dual-lens configuration is commonly available on mobile devices
nowadays, which naturally stores a pair of wide-angle and telephoto images of the same …

Image super-resolution (SR) with generative adversarial networks (GAN) has achieved great
success in restoring realistic details. However, it is notorious that GAN-based SR models will …

Super-resolution imaging has, for more than seventy years, gradually evolved to produce
advanced methods for enhancing the resolution of images beyond the diffraction limits …

We propose the first reference-based video super-resolution (RefVSR) approach that utilizes
reference videos for high-fidelity results. We focus on RefVSR in a triple-camera setting …