NTIRE 2021 challenge on image deblurring
Motion blur is a common photography artifact in dynamic environments that typically comes
jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on …
jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on …
Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration
Image restoration is a long-standing problem in low-level computer vision with many
interesting applications. We describe a flexible learning framework based on the concept of …
interesting applications. We describe a flexible learning framework based on the concept of …
MFQE 2.0: A new approach for multi-frame quality enhancement on compressed video
The past few years have witnessed great success in applying deep learning to enhance the
quality of compressed image/video. The existing approaches mainly focus on enhancing the …
quality of compressed image/video. The existing approaches mainly focus on enhancing the …
Multi-frame quality enhancement for compressed video
The past few years have witnessed great success in applying deep learning to enhance the
quality of compressed image/video. The existing approaches mainly focus on enhancing the …
quality of compressed image/video. The existing approaches mainly focus on enhancing the …
An end-to-end compression framework based on convolutional neural networks
Deep learning, eg, convolutional neural networks (CNNs), has achieved great success in
image processing and computer vision especially in high-level vision applications, such as …
image processing and computer vision especially in high-level vision applications, such as …
On learning optimized reaction diffusion processes for effective image restoration
For several decades, image restoration remains an active research topic in low-level
computer vision and hence new approaches are constantly emerging. However, many …
computer vision and hence new approaches are constantly emerging. However, many …
Deep generative adversarial compression artifact removal
Compression artifacts arise in images whenever a lossy compression algorithm is applied.
These artifacts eliminate details present in the original image, or add noise and small …
These artifacts eliminate details present in the original image, or add noise and small …
Spatio-temporal deformable convolution for compressed video quality enhancement
Recent years have witnessed remarkable success of deep learning methods in quality
enhancement for compressed video. To better explore temporal information, existing …
enhancement for compressed video. To better explore temporal information, existing …
Enhancing quality for HEVC compressed videos
The latest High Efficiency Video Coding (HEVC) standard has been increasingly applied to
generate video streams over the Internet. However, HEVC compressed videos may incur …
generate video streams over the Internet. However, HEVC compressed videos may incur …
CAS-CNN: A deep convolutional neural network for image compression artifact suppression
Lossy image compression algorithms are pervasively used to reduce the size of images
transmitted over the web and recorded on data storage media. However, we pay for their …
transmitted over the web and recorded on data storage media. However, we pay for their …