419 citations · 777 across the 33 of their papers we have counts for
29 papers · 1 filter
NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results
Longguang Wang, Yulan Guo, Juncheng Li +6
This paper summarizes the 3rd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of this challenge is to super-resolve a low-…
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
Xiaoning Liu, Zongwei Wu, Ao Li +109
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective netw…
Single Image Depth Prediction Made Better: A Multivariate Gaussian Take
Ce Liu, Suryansh Kumar, Shuhang Gu +2
Neural-network-based single image depth prediction (SIDP) is a challenging task where the goal is to predict the scene's per-pixel depth at test time. Since the problem, by definit…
VA-DepthNet: A Variational Approach to Single Image Depth Prediction
Ce Liu, Suryansh Kumar, Shuhang Gu +2
We introduce VA-DepthNet, a simple, effective, and accurate deep neural network approach for the single-image depth prediction (SIDP) problem. The proposed approach advocates using…
Revisiting Random Channel Pruning for Neural Network Compression
Yawei Li, Kamil Adamczewski, Wen Li +3
Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of neural networks. There has been a flurry of algorithms that try to solve this practical pro…
Measuring Perceptual Color Differences of Smartphone Photographs
Zhihua Wang, Keshuo Xu, Yang Yang +5
Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophys…
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results
Longguang Wang, Yulan Guo, Yingqian Wang +3
In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new sol…
Improving Facial Attribute Recognition by Group and Graph Learning
Zhenghao Chen, Shuhang Gu, Feng Zhu +2
Exploiting the relationships between attributes is a key challenge for improving multiple facial attribute recognition. In this work, we are concerned with two types of correlation…
Flow-based Kernel Prior with Application to Blind Super-Resolution
Jingyun Liang, Kai Zhang, Shuhang Gu +2
Kernel estimation is generally one of the key problems for blind image super-resolution (SR). Recently, Double-DIP proposes to model the kernel via a network architecture prior, wh…
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results
Dario Fuoli, Zhiwu Huang, Shuhang Gu +23
This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020. Common scaling factors for learned video super-resolution (VSR)…
Video Super-Resolution with Recurrent Structure-Detail Network
Takashi Isobe, Xu Jia, Shuhang Gu +3
Most video super-resolution methods super-resolve a single reference frame with the help of neighboring frames in a temporal sliding window. They are less efficient compared to the…
You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network
Boyun Li, Yuanbiao Gou, Shuhang Gu +3
In this paper, we study two challenging and less-touched problems in single image dehazing, namely, how to make deep learning achieve image dehazing without training on the ground-…
The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures
Yawei Li, Wen Li, Martin Danelljan +4
In this paper, we tackle the problem of convolutional neural network design. Instead of focusing on the design of the overall architecture, we investigate a design space that is us…
Flexible Example-based Image Enhancement with Task Adaptive Global Feature Self-Guided Network
Dario Kneubuehler, Shuhang Gu, Luc Van Gool +1
We propose the first practical multitask image enhancement network, that is able to learn one-to-many and many-to-one image mappings. We show that our model outperforms the current…
Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning
Kangning Liu, Shuhang Gu, Andres Romero +1
Existing unsupervised video-to-video translation methods fail to produce translated videos which are frame-wise realistic, semantic information preserving and video-level consisten…
Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training
Yunxuan Wei, Shuhang Gu, Yawei Li +1
These days, unsupervised super-resolution (SR) has been soaring due to its practical and promising potential in real scenarios. The philosophy of off-the-shelf approaches lies in t…
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
Yawei Li, Shuhang Gu, Christoph Mayer +2
In this paper, we analyze two popular network compression techniques, i.e. filter pruning and low-rank decomposition, in a unified sense. By simply changing the way the sparsity re…
DHP: Differentiable Meta Pruning via HyperNetworks
Yawei Li, Shuhang Gu, Kai Zhang +2
Network pruning has been the driving force for the acceleration of neural networks and the alleviation of model storage/transmission burden. With the advent of AutoML and neural ar…
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr, Martin Danelljan, Radu Timofte +18
This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting,…
Learning Filter Basis for Convolutional Neural Network Compression
Yawei Li, Shuhang Gu, Luc Van Gool +1
Convolutional neural networks (CNNs) based solutions have achieved state-of-the-art performances for many computer vision tasks, including classification and super-resolution of im…
Exemplar Guided Face Image Super-Resolution without Facial Landmarks
Berk Dogan, Shuhang Gu, Radu Timofte
Nowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images. Therefore, for super-resolving a given very low-resolutio…
Learning Content-Weighted Deep Image Compression
Mu Li, Wangmeng Zuo, Shuhang Gu +2
Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance. Most existing methods adopt spatially invariant bit length allocation…
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
Andrey Ignatov, Radu Timofte, Thang Van Vu +45
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tra…
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks
Shuhang Gu, Radu Timofte, Luc Van Gool
Tremendous advances in image restoration tasks such as denoising and super-resolution have been achieved using neural networks. Such approaches generally employ very deep architect…