2 code implementations • CVPR 2017 • Kai Zhang, WangMeng Zuo, Shuhang Gu, Lei Zhang
Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e. g., deblurring).
Ranked #1 on Color Image Denoising on BSD68 sigma5
3 code implementations • 22 Apr 2024 • Xiaoning Liu, Zongwei Wu, Ao Li, Florin-Alexandru Vasluianu, Yulun Zhang, Shuhang Gu, Le Zhang, Ce Zhu, Radu Timofte, Zhi Jin, Hongjun Wu, Chenxi Wang, Haitao Ling, Yuanhao Cai, Hao Bian, Yuxin Zheng, Jing Lin, Alan Yuille, Ben Shao, Jin Guo, Tianli Liu, Mohao Wu, Yixu Feng, Shuo Hou, Haotian Lin, Yu Zhu, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang, Qingsen Yan, Wenbin Zou, Weipeng Yang, Yunxiang Li, Qiaomu Wei, Tian Ye, Sixiang Chen, Zhao Zhang, Suiyi Zhao, Bo wang, Yan Luo, Zhichao Zuo, Mingshen Wang, Junhu Wang, Yanyan Wei, Xiaopeng Sun, Yu Gao, Jiancheng Huang, Hongming Chen, Xiang Chen, Hui Tang, Yuanbin Chen, Yuanbo Zhou, Xinwei Dai, Xintao Qiu, Wei Deng, Qinquan Gao, Tong Tong, Mingjia Li, Jin Hu, Xinyu He, Xiaojie Guo, sabarinathan, K Uma, A Sasithradevi, B Sathya Bama, S. Mohamed Mansoor Roomi, V. Srivatsav, Jinjuan Wang, Long Sun, Qiuying Chen, Jiahong Shao, Yizhi Zhang, Marcos V. Conde, Daniel Feijoo, Juan C. Benito, Alvaro García, Jaeho Lee, Seongwan Kim, Sharif S M A, Nodirkhuja Khujaev, Roman Tsoy, Ali Murtaza, Uswah Khairuddin, Ahmad 'Athif Mohd Faudzi, Sampada Malagi, Amogh Joshi, Nikhil Akalwadi, Chaitra Desai, Ramesh Ashok Tabib, Uma Mudenagudi, Wenyi Lian, Wenjing Lian, Jagadeesh Kalyanshetti, Vijayalaxmi Ashok Aralikatti, Palani Yashaswini, Nitish Upasi, Dikshit Hegde, Ujwala Patil, Sujata C, Xingzhuo Yan, Wei Hao, Minghan Fu, Pooja Choksy, Anjali Sarvaiya, Kishor Upla, Kiran Raja, Hailong Yan, Yunkai Zhang, Baiang Li, Jingyi Zhang, Huan Zheng
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
2 code implementations • 4 Nov 2019 • Kai Zhang, Shuhang Gu, Radu Timofte, Zheng Hui, Xiumei Wang, Xinbo Gao, Dongliang Xiong, Shuai Liu, Ruipeng Gang, Nan Nan, Chenghua Li, Xueyi Zou, Ning Kang, Zhan Wang, Hang Xu, Chaofeng Wang, Zheng Li, Lin-Lin Wang, Jun Shi, Wenyu Sun, Zhiqiang Lang, Jiangtao Nie, Wei Wei, Lei Zhang, Yazhe Niu, Peijin Zhuo, Xiangzhen Kong, Long Sun, Wenhao Wang
The challenge had 3 tracks.
1 code implementation • CVPR 2021 • Yunxuan Wei, Shuhang Gu, Yawei Li, Longcun Jin
The philosophy of off-the-shelf approaches lies in the augmentation of unpaired data, i. e. first generating synthetic low-resolution (LR) images $\mathcal{Y}^g$ corresponding to real-world high-resolution (HR) images $\mathcal{X}^r$ in the real-world LR domain $\mathcal{Y}^r$, and then utilizing the pseudo pairs $\{\mathcal{Y}^g, \mathcal{X}^r\}$ for training in a supervised manner.
1 code implementation • 18 Nov 2019 • Andreas Lugmayr, Martin Danelljan, Radu Timofte, Manuel Fritsche, Shuhang Gu, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, A. N. Rajagopalan, Nam Hyung Joon, Yu Seung Won, Guisik Kim, Dokyeong Kwon, Chih-Chung Hsu, Chia-Hsiang Lin, Yuanfei Huang, Xiaopeng Sun, Wen Lu, Jie Li, Xinbo Gao, Sefi Bell-Kligler
For training, only one set of source input images is therefore provided in the challenge.
1 code implementation • 18 Nov 2019 • Manuel Fritsche, Shuhang Gu, Radu Timofte
Furthermore, we propose to separate the low and high image frequencies and treat them differently during training.
1 code implementation • CVPR 2021 • Jingyun Liang, Kai Zhang, Shuhang Gu, Luc van Gool, Radu Timofte
Kernel estimation is generally one of the key problems for blind image super-resolution (SR).
2 code implementations • 13 Feb 2023 • Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Luc van Gool
While state-of-the-art deep neural network methods for SIDP learn the scene depth from images in a supervised setting, they often overlook the invaluable invariances and priors in the rigid scene space, such as the regularity of the scene.
Ranked #18 on Monocular Depth Estimation on NYU-Depth V2
2 code implementations • ECCV 2020 • Takashi Isobe, Xu Jia, Shuhang Gu, Songjiang Li, Shengjin Wang, Qi Tian
Most video super-resolution methods super-resolve a single reference frame with the help of neighboring frames in a temporal sliding window.
1 code implementation • 17 Sep 2019 • Dario Fuoli, Shuhang Gu, Radu Timofte
However, as the motion estimation problem is a highly challenging problem, inaccurate motion compensation may affect the performance of VSR algorithms.
Video Super-Resolution Image and Video Processing
3 code implementations • ICCV 2019 • Yawei Li, Shuhang Gu, Luc van Gool, Radu Timofte
Convolutional neural networks (CNNs) based solutions have achieved state-of-the-art performances for many computer vision tasks, including classification and super-resolution of images.
2 code implementations • CVPR 2020 • Yawei Li, Shuhang Gu, Christoph Mayer, Luc van Gool, Radu Timofte
In this paper, we analyze two popular network compression techniques, i. e. filter pruning and low-rank decomposition, in a unified sense.
1 code implementation • 16 Jan 2024 • Leheng Zhang, Yawei Li, Xingyu Zhou, Xiaorui Zhao, Shuhang Gu
The introduced token dictionary could learn prior information from training data and adapt the learned prior to specific testing image through an adaptive refinement step.
Ranked #1 on Image Super-Resolution on Set14
2 code implementations • ECCV 2020 • Yawei Li, Shuhang Gu, Kai Zhang, Luc van Gool, Radu Timofte
Passing the sparsified latent vectors through the hypernetworks, the corresponding slices of the generated weight parameters can be removed, achieving the effect of network pruning.
1 code implementation • 30 Jun 2020 • Boyun Li, Yuanbiao Gou, Shuhang Gu, Jerry Zitao Liu, Joey Tianyi Zhou, Xi Peng
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-truth clean image (unsupervised) and a image collection (untrained).
1 code implementation • ICCV 2019 • Shuhang Gu, Yawei Li, Luc Van Gool, Radu Timofte
During the past years, tremendous advances in image restoration tasks have been achieved using highly complex neural networks.
1 code implementation • 12 Jan 2024 • Xingyu Zhou, Leheng Zhang, Xiaorui Zhao, Keze Wang, Leida Li, Shuhang Gu
The core of MIA-VSR is leveraging feature-level temporal continuity between adjacent frames to reduce redundant computations and make more rational use of previously enhanced SR features.
1 code implementation • CVPR 2018 • Mu Li, WangMeng Zuo, Shuhang Gu, Debin Zhao, David Zhang
Therefore, the encoder, decoder, binarizer and importance map can be jointly optimized in an end-to-end manner by using a subset of the ImageNet database.
1 code implementation • 26 May 2022 • Zhihua Wang, Keshuo Xu, Yang Yang, Jianlei Dong, Shuhang Gu, Lihao Xu, Yuming Fang, Kede Ma
Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography.
1 code implementation • 15 Jan 2024 • Kexuan Shi, Xingyu Zhou, Shuhang Gu
We evaluate the proposed Fourier reparameterization method on different INR tasks with various MLP architectures, including vanilla MLP, MLP with positional encoding and MLP with advanced activation function, etc.
1 code implementation • 17 Jun 2019 • 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.
Ranked #1 on Image Super-Resolution on VggFace2 - 8x upscaling
1 code implementation • 1 Apr 2019 • Mu Li, WangMeng Zuo, Shuhang Gu, Jane You, David Zhang
Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance.
1 code implementation • ICCV 2019 • Shuhang Gu, Wen Li, Luc Van Gool, Radu Timofte
Tremendous advances in image restoration tasks such as denoising and super-resolution have been achieved using neural networks.
1 code implementation • CVPR 2021 • Yawei Li, Wen Li, Martin Danelljan, Kai Zhang, Shuhang Gu, Luc van Gool, Radu Timofte
Based on that, we articulate the heterogeneity hypothesis: with the same training protocol, there exists a layer-wise differentiated network architecture (LW-DNA) that can outperform the original network with regular channel configurations but with a lower level of model complexity.
1 code implementation • CVPR 2022 • Yawei Li, Kamil Adamczewski, Wen Li, Shuhang Gu, Radu Timofte, Luc van Gool
The proposed approach provides a new way to compare different methods, namely how well they behave compared with random pruning.
no code implementations • 15 Jan 2018 • Mu Li, Shuhang Gu, David Zhang, WangMeng Zuo
One key issue of arithmetic encoding method is to predict the probability of the current coding symbol from its context, i. e., the preceding encoded symbols, which usually can be executed by building a look-up table (LUT).
no code implementations • 3 Dec 2015 • Yuan Xie, Shuhang Gu, Yan Liu, WangMeng Zuo, Wensheng Zhang, Lei Zhang
However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications.
no code implementations • 23 May 2014 • Qi Xie, Deyu Meng, Shuhang Gu, Lei Zhang, WangMeng Zuo, Xiangchu Feng, Zongben Xu
Nevertheless, so far the global optimal solution of WNNM problem is not completely solved yet due to its non-convexity in general cases.
no code implementations • 30 Jul 2018 • 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.
no code implementations • 3 Oct 2018 • Andrey Ignatov, Radu Timofte, Thang Van Vu, Tung Minh Luu, Trung X. Pham, Cao Van Nguyen, Yongwoo Kim, Jae-Seok Choi, Munchurl Kim, Jie Huang, Jiewen Ran, Chen Xing, Xingguang Zhou, Pengfei Zhu, Mingrui Geng, Yawei Li, Eirikur Agustsson, Shuhang Gu, Luc van Gool, Etienne de Stoutz, Nikolay Kobyshev, Kehui Nie, Yan Zhao, Gen Li, Tong Tong, Qinquan Gao, Liu Hanwen, Pablo Navarrete Michelini, Zhu Dan, Hu Fengshuo, Zheng Hui, Xiumei Wang, Lirui Deng, Rang Meng, Jinghui Qin, Yukai Shi, Wushao Wen, Liang Lin, Ruicheng Feng, Shixiang Wu, Chao Dong, Yu Qiao, Subeesh Vasu, Nimisha Thekke Madam, Praveen Kandula, A. N. Rajagopalan, Jie Liu, Cheolkon Jung
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
no code implementations • NeurIPS 2014 • Shuhang Gu, Lei Zhang, WangMeng Zuo, Xiangchu Feng
Discriminative dictionary learning (DL) has been widely studied in various pattern classification problems.
no code implementations • CVPR 2018 • Minghan Li, Qi Xie, Qian Zhao, Wei Wei, Shuhang Gu, Jing Tao, Deyu Meng
Based on such understanding, we specifically formulate both characteristics into a multiscale convolutional sparse coding (MS-CSC) model for the video rain streak removal task.
no code implementations • CVPR 2014 • Shuhang Gu, Lei Zhang, WangMeng Zuo, Xiangchu Feng
In this paper we study the weighted nuclear norm minimization (WNNM) problem, where the singular values are assigned different weights.
no code implementations • CVPR 2015 • Wangmeng Zuo, Dongwei Ren, Shuhang Gu, Liang Lin, Lei Zhang
The maximum a posterior (MAP)-based blind deconvolution framework generally involves two stages: blur kernel estimation and non-blind restoration.
no code implementations • CVPR 2016 • Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu, Shuhang Gu, WangMeng Zuo, Lei Zhang
Multispectral images (MSI) can help deliver more faithful representation for real scenes than the traditional image system, and enhance the performance of many computer vision tasks.
no code implementations • CVPR 2016 • Keze Wang, Liang Lin, WangMeng Zuo, Shuhang Gu, Lei Zhang
Feature representation and object category classification are two key components of most object detection methods.
no code implementations • CVPR 2017 • Shuhang Gu, WangMeng Zuo, Shi Guo, Yunjin Chen, Chongyu Chen, Lei Zhang
To address these limitations, we propose a weighted analysis representation model for guided depth image enhancement, which advances the conventional methods in two aspects: (i) task driven learning and (ii) dynamic guidance.
no code implementations • ICCV 2015 • Shuhang Gu, WangMeng Zuo, Qi Xie, Deyu Meng, Xiangchu Feng, Lei Zhang
Sparse coding (SC) plays an important role in versatile computer vision applications such as image super-resolution (SR).
no code implementations • ICCV 2017 • Shuhang Gu, Deyu Meng, WangMeng Zuo, Lei Zhang
To exploit the complementary representation mechanisms of ASR and SSR, we integrate the two models and propose a joint convolutional analysis and synthesis (JCAS) sparse representation model.
no code implementations • 14 Apr 2020 • Kangning Liu, Shuhang Gu, Andres Romero, Radu Timofte
Existing unsupervised video-to-video translation methods fail to produce translated videos which are frame-wise realistic, semantic information preserving and video-level consistent.
no code implementations • 3 May 2020 • Kai Zhang, Shuhang Gu, Radu Timofte, Taizhang Shang, Qiuju Dai, Shengchen Zhu, Tong Yang, Yandong Guo, Younghyun Jo, Sejong Yang, Seon Joo Kim, Lin Zha, Jiande Jiang, Xinbo Gao, Wen Lu, Jing Liu, Kwangjin Yoon, Taegyun Jeon, Kazutoshi Akita, Takeru Ooba, Norimichi Ukita, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Dongliang He, Wenhao Wu, Yukang Ding, Chao Li, Fu Li, Shilei Wen, Jianwei Li, Fuzhi Yang, Huan Yang, Jianlong Fu, Byung-Hoon Kim, JaeHyun Baek, Jong Chul Ye, Yuchen Fan, Thomas S. Huang, Junyeop Lee, Bokyeung Lee, Jungki Min, Gwantae Kim, Kanghyu Lee, Jaihyun Park, Mykola Mykhailych, Haoyu Zhong, Yukai Shi, Xiaojun Yang, Zhijing Yang, Liang Lin, Tongtong Zhao, Jinjia Peng, Huibing Wang, Zhi Jin, Jiahao Wu, Yifu Chen, Chenming Shang, Huanrong Zhang, Jeongki Min, Hrishikesh P. S, Densen Puthussery, Jiji C. V
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.
no code implementations • 13 May 2020 • Dario Kneubuehler, Shuhang Gu, Luc van Gool, Radu Timofte
We propose the first practical multitask image enhancement network, that is able to learn one-to-many and many-to-one image mappings.
no code implementations • ECCV 2020 • Zhihao Hu, Zhenghao Chen, Dong Xu, Guo Lu, Wanli Ouyang, Shuhang Gu
In this work, we propose a new framework called Resolution-adaptive Flow Coding (RaFC) to effectively compress the flow maps globally and locally, in which we use multi-resolution representations instead of single-resolution representations for both the input flow maps and the output motion features of the MV encoder.
no code implementations • 14 Sep 2020 • Dario Fuoli, Zhiwu Huang, Shuhang Gu, Radu Timofte, Arnau Raventos, Aryan Esfandiari, Salah Karout, Xuan Xu, Xin Li, Xin Xiong, Jinge Wang, Pablo Navarrete Michelini, Wen-Hao Zhang, Dongyang Zhang, Hanwei Zhu, Dan Xia, Haoyu Chen, Jinjin Gu, Zhi Zhang, Tongtong Zhao, Shanshan Zhao, Kazutoshi Akita, Norimichi Ukita, Hrishikesh P. S, Densen Puthussery, Jiji C. V
Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details.
no code implementations • 7 May 2021 • Jinjin Gu, Haoming Cai, Chao Dong, Jimmy S. Ren, Yu Qiao, Shuhang Gu, Radu Timofte, Manri Cheon, SungJun Yoon, Byungyeon Kang, Junwoo Lee, Qing Zhang, Haiyang Guo, Yi Bin, Yuqing Hou, Hengliang Luo, Jingyu Guo, ZiRui Wang, Hai Wang, Wenming Yang, Qingyan Bai, Shuwei Shi, Weihao Xia, Mingdeng Cao, Jiahao Wang, Yifan Chen, Yujiu Yang, Yang Li, Tao Zhang, Longtao Feng, Yiting Liao, Junlin Li, William Thong, Jose Costa Pereira, Ales Leonardis, Steven McDonagh, Kele Xu, Lehan Yang, Hengxing Cai, Pengfei Sun, Seyed Mehdi Ayyoubzadeh, Ali Royat, Sid Ahmed Fezza, Dounia Hammou, Wassim Hamidouche, Sewoong Ahn, Gwangjin Yoon, Koki Tsubota, Hiroaki Akutsu, Kiyoharu Aizawa
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
no code implementations • 17 May 2021 • Andrey Ignatov, Andres Romero, Heewon Kim, Radu Timofte, Chiu Man Ho, Zibo Meng, Kyoung Mu Lee, Yuxiang Chen, Yutong Wang, Zeyu Long, Chenhao Wang, Yifei Chen, Boshen Xu, Shuhang Gu, Lixin Duan, Wen Li, Wang Bofei, Zhang Diankai, Zheng Chengjian, Liu Shaoli, Gao Si, Zhang Xiaofeng, Lu Kaidi, Xu Tianyu, Zheng Hui, Xinbo Gao, Xiumei Wang, Jiaming Guo, Xueyi Zhou, Hao Jia, Youliang Yan
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services.
no code implementations • 28 May 2021 • Zhenghao Chen, Shuhang Gu, Feng Zhu, Jing Xu, Rui Zhao
For the spatial correlation, we aggregate attributes with spatial similarity into a part-based group and then introduce a Group Attention Learning to generate the group attention and the part-based group feature.
no code implementations • 27 Jul 2021 • Wenlong Cheng, Mingbo Zhao, Zhiling Ye, Shuhang Gu
In this paper, we propose a novel compression framework \textbf{M}ulti-scale \textbf{F}eature \textbf{A}ggregation Net based \textbf{GAN} (MFAGAN) for reducing the memory access cost of the generator.
Hardware Aware Neural Architecture Search Image Super-Resolution +1
no code implementations • 20 Apr 2022 • Longguang Wang, Yulan Guo, Yingqian Wang, Juncheng Li, Shuhang Gu, Radu Timofte
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 solutions and results.
no code implementations • CVPR 2023 • Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Luc van Gool
Accordingly, we introduce an approach that performs continuous modeling of per-pixel depth, where we can predict and reason about the per-pixel depth and its distribution.