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Found 14 papers, 12 papers with code
Reference-based Image and Video Super-Resolution via C2-Matching
To tackle these challenges, we propose C2-Matching in this work, which performs explicit robust matching crossing transformation and resolution.
GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond
While most existing perceptual-oriented approaches attempt to generate realistic outputs through learning with adversarial loss, our method, Generative LatEnt bANk (GLEAN), goes beyond existing practices by directly leveraging rich and diverse priors encapsulated in a pre-trained GAN.
Exploring CLIP for Assessing the Look and Feel of Images
Measuring the perception of visual content is a long-standing problem in computer vision.
Towards Robust Blind Face Restoration with Codebook Lookup Transformer
In this paper, we demonstrate that a learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration mapping by casting blind face restoration as a code prediction task, while providing rich visual atoms for generating high-quality faces.
Ranked #1 on
Blind Face Restoration
on WIDER
On the Generalization of BasicVSR++ to Video Deblurring and Denoising
The exploitation of long-term information has been a long-standing problem in video restoration.
Investigating Tradeoffs in Real-World Video Super-Resolution
The diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training.
Temporally Consistent Video Colorization with Deep Feature Propagation and Self-regularization Learning
We address this problem from a new perspective, by jointly considering colorization and temporal consistency in a unified framework.
Robust Reference-based Super-Resolution via C2-Matching
However, performing local transfer is difficult because of two gaps between input and reference images: the transformation gap (e. g. scale and rotation) and the resolution gap (e. g. HR and LR).
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
We show that by empowering the recurrent framework with the enhanced propagation and alignment, one can exploit spatiotemporal information across misaligned video frames more effectively.
Ranked #1 on
Video Enhancement
on MFQE v2
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension.
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution
We show that pre-trained Generative Adversarial Networks (GANs), e. g., StyleGAN, can be used as a latent bank to improve the restoration quality of large-factor image super-resolution (SR).
Understanding Deformable Alignment in Video Super-Resolution
Aside from the contributions to deformable alignment, our formulation inspires a more flexible approach to introduce offset diversity to flow-based alignment, improving its performance.
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges.
Ranked #2 on
Deblurring
on REDS
