Proceedings ArticleDOI
NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study
Seungjun Nah,Sungyong Baik,Seokil Hong,Gyeongsik Moon,Sanghyun Son,Radu Timofte,Kyoung Mu Lee +6 more
- pp 1996-2005
TLDR
It is found that the NTIRE 2019 challenges push the state-of-the-art in video deblurring and super-resolution, reaching compelling performance on the newly proposed REDS dataset.Abstract:
This paper introduces a novel large dataset for video deblurring, video super-resolution and studies the state-of-the-art as emerged from the NTIRE 2019 video restoration challenges. The video deblurring and video super-resolution challenges are each the first challenge of its kind, with 4 competitions, hundreds of participants and tens of proposed solutions. Our newly collected REalistic and Diverse Scenes dataset (REDS) was employed by the challenges. In our study, we compare the solutions from the challenges to a set of representative methods from the literature and evaluate them on our proposed REDS dataset. We find that the NTIRE 2019 challenges push the state-of-the-art in video deblurring and super-resolution, reaching compelling performance on our newly proposed REDS dataset.read more
Citations
More filters
Proceedings ArticleDOI
HINet: Half Instance Normalization Network for Image Restoration
TL;DR: Wang et al. as mentioned in this paper designed a simple and powerful multi-stage network named HINet, which consists of two subnetworks, and achieved state-of-the-art performance on various image restoration tasks.
Posted Content
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
TL;DR: A succinct pipeline is shown that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms and can serve as strong baselines for future VSR approaches.
Book ChapterDOI
Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms
TL;DR: This work presents a large-scale dataset of real-world blurred images and ground truth sharp images for learning and benchmarking single image deblurring methods, and develops a postprocessing method to produce high-quality ground truth images.
Posted Content
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
TL;DR: Zhang et al. as mentioned in this paper proposed a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address the challenges of aligning multiple frames given large motions and effectively fusing different frames with diverse motion and blur.
Posted Content
A Deep Journey into Super-resolution: A survey.
TL;DR: A taxonomy for deep-learning based super-resolution networks is introduced that groups existing methods into nine categories including linear, residual, multi-branch, recursive, progressive, attention-based and adversarial designs and identifies several shortcomings of existing techniques.
References
More filters
Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Proceedings ArticleDOI
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig,Lucas Theis,Ferenc Huszar,Jose Caballero,Andrew Cunningham,Alejandro Acosta,Andrew Peter Aitken,Alykhan Tejani,Johannes Totz,Zehan Wang,Wenzhe Shi +10 more
TL;DR: SRGAN as mentioned in this paper proposes a perceptual loss function which consists of an adversarial loss and a content loss, which pushes the solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.
Book ChapterDOI
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
TL;DR: In this paper, the authors combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image style transfer, where a feedforward network is trained to solve the optimization problem proposed by Gatys et al. in real-time.
Posted Content
CBAM: Convolutional Block Attention Module
TL;DR: The proposed Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks, can be integrated into any CNN architectures seamlessly with negligible overheads and is end-to-end trainable along with base CNNs.
Posted Content
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
TL;DR: This work considers image transformation problems, and proposes the use of perceptual loss functions for training feed-forward networks for image transformation tasks, and shows results on image style transfer, where aFeed-forward network is trained to solve the optimization problem proposed by Gatys et al. in real-time.