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40 results on '"Michelini, Pablo Navarrete"'

1. NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

Author

Yang, Ren, Timofte, Radu, Zheng, Meisong, Xing, Qunliang, Qiao, Minglang, Xu, Mai, Jiang, Lai, Liu, Huaida, Chen, Ying, Ben, Youcheng, Zhou, Xiao, Fu, Chen, Cheng, Pei, Yu, Gang, Li, Junyi, Wu, Renlong, Zhang, Zhilu, Shang, Wei, Lv, Zhengyao, Chen, Yunjin, Zhou, Mingcai, Ren, Dongwei, Zhang, Kai, Zuo, Wangmeng, Ostyakov, Pavel, Dmitry, Vyal, Soltanayev, Shakarim, Sergey, Chervontsev, Magauiya, Zhussip, Zou, Xueyi, Yan, Youliang, Michelini, Pablo Navarrete, Lu, Yunhua, Zhang, Diankai, Liu, Shaoli, Gao, Si, Wu, Biao, Zheng, Chengjian, Zhang, Xiaofeng, Lu, Kaidi, Wang, Ning, Canh, Thuong Nguyen, Bach, Thong, Wang, Qing, Sun, Xiaopeng, Ma, Haoyu, Zhao, Shijie, Li, Junlin, Xie, Liangbin, Shi, Shuwei, Yang, Yujiu, Wang, Xintao, Gu, Jinjin, Dong, Chao, Shi, Xiaodi, Nian, Chunmei, Jiang, Dong, Lin, Jucai, Xie, Zhihuai, Ye, Mao, Luo, Dengyan, Peng, Liuhan, Chen, Shengjie, Liu, Xin, Wang, Qian, Liang, Boyang, Dong, Hang, Huang, Yuhao, Chen, Kai, Guo, Xingbei, Sun, Yujing, Wu, Huilei, Wei, Pengxu, Huang, Yulin, Chen, Junying, Lee, Ik Hyun, Khowaja, Sunder Ali, and Yoon, Jiseok

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the super-resolution and quality enhancement of HEVC compressed video. They require x2 and x4 super-resolution, respectively. The three tracks totally attract more than 600 registrations. In the test phase, 8 teams, 8 teams and 12 teams submitted the final results to Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution and quality enhancement of compressed video. The proposed LDV 2.0 dataset is available at https://github.com/RenYang-home/LDV_dataset. The homepage of this challenge (including open-sourced codes) is at https://github.com/RenYang-home/NTIRE22_VEnh_SR.

Published
2022

2. edge-SR: Super-Resolution For The Masses

Author

Michelini, Pablo Navarrete, Lu, Yunhua, and Jiang, Xingqun

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Signal Processing
Abstract

Classic image scaling (e.g. bicubic) can be seen as one convolutional layer and a single upscaling filter. Its implementation is ubiquitous in all display devices and image processing software. In the last decade deep learning systems have been introduced for the task of image super-resolution (SR), using several convolutional layers and numerous filters. These methods have taken over the benchmarks of image quality for upscaling tasks. Would it be possible to replace classic upscalers with deep learning architectures on edge devices such as display panels, tablets, laptop computers, etc.? On one hand, the current trend in Edge-AI chips shows a promising future in this direction, with rapid development of hardware that can run deep-learning tasks efficiently. On the other hand, in image SR only few architectures have pushed the limit to extreme small sizes that can actually run on edge devices at real-time. We explore possible solutions to this problem with the aim to fill the gap between classic upscalers and small deep learning configurations. As a transition from classic to deep-learning upscaling we propose edge-SR (eSR), a set of one-layer architectures that use interpretable mechanisms to upscale images. Certainly, a one-layer architecture cannot reach the quality of deep learning systems. Nevertheless, we find that for high speed requirements, eSR becomes better at trading-off image quality and runtime performance. Filling the gap between classic and deep-learning architectures for image upscaling is critical for massive adoption of this technology. It is equally important to have an interpretable system that can reveal the inner strategies to solve this problem and guide us to future improvements and better understanding of larger networks., Comment: In WACV 2022. Code available in https://github.com/pnavarre/eSR

Published
2021

3. NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

Author

Yang, Ren, Timofte, Radu, Liu, Jing, Xu, Yi, Zhang, Xinjian, Zhao, Minyi, Zhou, Shuigeng, Chan, Kelvin C. K., Zhou, Shangchen, Xu, Xiangyu, Loy, Chen Change, Li, Xin, Liu, Fanglong, Zheng, He, Jiang, Lielin, Zhang, Qi, He, Dongliang, Li, Fu, Dang, Qingqing, Huang, Yibin, Maggioni, Matteo, Fu, Zhongqian, Xiao, Shuai, li, Cheng, Tanay, Thomas, Song, Fenglong, Chao, Wentao, Guo, Qiang, Liu, Yan, Li, Jiang, Qu, Xiaochao, Hou, Dewang, Yang, Jiayu, Jiang, Lyn, You, Di, Zhang, Zhenyu, Mou, Chong, Koshelev, Iaroslav, Ostyakov, Pavel, Somov, Andrey, Hao, Jia, Zou, Xueyi, Zhao, Shijie, Sun, Xiaopeng, Liao, Yiting, Zhang, Yuanzhi, Wang, Qing, Zhan, Gen, Guo, Mengxi, Li, Junlin, Lu, Ming, Ma, Zhan, Michelini, Pablo Navarrete, Wang, Hai, Chen, Yiyun, Guo, Jingyu, Zhang, Liliang, Yang, Wenming, Kim, Sijung, Oh, Syehoon, Wang, Yucong, Cai, Minjie, Hao, Wei, Shi, Kangdi, Li, Liangyan, Chen, Jun, Gao, Wei, Liu, Wang, Zhang, Xiaoyu, Zhou, Linjie, Lin, Sixin, and Wang, Ru

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results. In this challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The challenge has three tracks. Tracks 1 and 2 aim at enhancing the videos compressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the videos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement of Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets at enhancing the perceptual quality. The three tracks totally attract 482 registrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the final results of Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of video quality enhancement. The homepage of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh, Comment: Corrected the MOS values in Table 2, and corrected some minor typos

Published
2021

4. Back-Projection Pipeline

Author

Michelini, Pablo Navarrete, Liu, Hanwen, Lu, Yunhua, and Jiang, Xingqun

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

We propose a simple extension of residual networks that works simultaneously in multiple resolutions. Our network design is inspired by the iterative back-projection algorithm but seeks the more difficult task of learning how to enhance images. Compared to similar approaches, we propose a novel solution to make back-projections run in multiple resolutions by using a data pipeline workflow. Features are updated at multiple scales in each layer of the network. The update dynamic through these layers includes interactions between different resolutions in a way that is causal in scale, and it is represented by a system of ODEs, as opposed to a single ODE in the case of ResNets. The system can be used as a generic multi-resolution approach to enhance images. We test it on several challenging tasks with special focus on super-resolution and raindrop removal. Our results are competitive with state-of-the-arts and show a strong ability of our system to learn both global and local image features.

Published
2021

5. Multi-Grid Back-Projection Networks

Author

Michelini, Pablo Navarrete, Chen, Wenbin, Liu, Hanwen, Zhu, Dan, and Jiang, Xingqun

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Multi-Grid Back-Projection (MGBP) is a fully-convolutional network architecture that can learn to restore images and videos with upscaling artifacts. Using the same strategy of multi-grid partial differential equation (PDE) solvers this multiscale architecture scales computational complexity efficiently with increasing output resolutions. The basic processing block is inspired in the iterative back-projection (IBP) algorithm and constitutes a type of cross-scale residual block with feedback from low resolution references. The architecture performs in par with state-of-the-arts alternatives for regression targets that aim to recover an exact copy of a high resolution image or video from which only a downscale image is known. A perceptual quality target aims to create more realistic outputs by introducing artificial changes that can be different from a high resolution original content as long as they are consistent with the low resolution input. For this target we propose a strategy using noise inputs in different resolution scales to control the amount of artificial details generated in the output. The noise input controls the amount of innovation that the network uses to create artificial realistic details. The effectiveness of this strategy is shown in benchmarks and it is explained as a particular strategy to traverse the perception-distortion plane., Comment: Accepted for publication in IEEE Journal of Selected Topics in Signal Processing (J-STSP). arXiv admin note: text overlap with arXiv:1809.10711

Published
2020

6. AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results

Author

Fuoli, Dario, Huang, Zhiwu, Gu, Shuhang, Timofte, Radu, Raventos, Arnau, Esfandiari, Aryan, Karout, Salah, Xu, Xuan, Li, Xin, Xiong, Xin, Wang, Jinge, Michelini, Pablo Navarrete, Zhang, Wenhao, Zhang, Dongyang, Zhu, Hanwei, Xia, Dan, Chen, Haoyu, Gu, Jinjin, Zhang, Zhi, Zhao, Tongtong, Zhao, Shanshan, Akita, Kazutoshi, Ukita, Norimichi, S, Hrishikesh P, Puthussery, Densen, and C V, Jiji

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

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) do not go beyond factor 4. Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details. The task in this challenge is to upscale videos with an extreme factor of 16, which results in more serious degradations that also affect the structural integrity of the videos. A single pixel in the low-resolution (LR) domain corresponds to 256 pixels in the high-resolution (HR) domain. Due to this massive information loss, it is hard to accurately restore the missing information. Track 1 is set up to gauge the state-of-the-art for such a demanding task, where fidelity to the ground truth is measured by PSNR and SSIM. Perceptually higher quality can be achieved in trade-off for fidelity by generating plausible high-frequency content. Track 2 therefore aims at generating visually pleasing results, which are ranked according to human perception, evaluated by a user study. In contrast to single image super-resolution (SISR), VSR can benefit from additional information in the temporal domain. However, this also imposes an additional requirement, as the generated frames need to be consistent along time.

Published
2020

7. NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

Author

Abdelhamed, Abdelrahman, Afifi, Mahmoud, Timofte, Radu, Brown, Michael S., Cao, Yue, Zhang, Zhilu, Zuo, Wangmeng, Zhang, Xiaoling, Liu, Jiye, Chen, Wendong, Wen, Changyuan, Liu, Meng, Lv, Shuailin, Zhang, Yunchao, Pan, Zhihong, Li, Baopu, Xi, Teng, Fan, Yanwen, Yu, Xiyu, Zhang, Gang, Liu, Jingtuo, Han, Junyu, Ding, Errui, Yu, Songhyun, Park, Bumjun, Jeong, Jechang, Liu, Shuai, Zong, Ziyao, Nan, Nan, Li, Chenghua, Yang, Zengli, Bao, Long, Wang, Shuangquan, Bai, Dongwoon, Lee, Jungwon, Kim, Youngjung, Rho, Kyeongha, Shin, Changyeop, Kim, Sungho, Tang, Pengliang, Zhao, Yiyun, Zhou, Yuqian, Fan, Yuchen, Huang, Thomas, Li, Zhihao, Shah, Nisarg A., Liu, Wei, Yan, Qiong, Zhao, Yuzhi, Możejko, Marcin, Latkowski, Tomasz, Treszczotko, Lukasz, Szafraniuk, Michał, Trojanowski, Krzysztof, Wu, Yanhong, Michelini, Pablo Navarrete, Hu, Fengshuo, Lu, Yunhua, Kim, Sujin, Kim, Wonjin, Lee, Jaayeon, Choi, Jang-Hwan, Zhussip, Magauiya, Khassenov, Azamat, Kim, Jong Hyun, Cho, Hwechul, Kansal, Priya, Nathan, Sabari, Ye, Zhangyu, Lu, Xiwen, Wu, Yaqi, Yang, Jiangxin, Cao, Yanlong, Tang, Siliang, Cao, Yanpeng, Maggioni, Matteo, Marras, Ioannis, Tanay, Thomas, Slabaugh, Gregory, Yan, Youliang, Kang, Myungjoo, Choi, Han-Soo, Song, Kyungmin, Xu, Shusong, Lu, Xiaomu, Wang, Tingniao, Lei, Chunxia, Liu, Bin, Gupta, Rajat, and Kumar, Vineet

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ~250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://bit.ly/siddplus_data.

Published
2020

8. NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results

Author

Lugmayr, Andreas, Danelljan, Martin, Timofte, Radu, Ahn, Namhyuk, Bai, Dongwoon, Cai, Jie, Cao, Yun, Chen, Junyang, Cheng, Kaihua, Chun, SeYoung, Deng, Wei, El-Khamy, Mostafa, Ho, Chiu Man, Ji, Xiaozhong, Kheradmand, Amin, Kim, Gwantae, Ko, Hanseok, Lee, Kanghyu, Lee, Jungwon, Li, Hao, Liu, Ziluan, Liu, Zhi-Song, Liu, Shuai, Lu, Yunhua, Meng, Zibo, Michelini, Pablo Navarrete, Micheloni, Christian, Prajapati, Kalpesh, Ren, Haoyu, Seo, Yong Hyeok, Siu, Wan-Chi, Sohn, Kyung-Ah, Tai, Ying, Umer, Rao Muhammad, Wang, Shuangquan, Wang, Huibing, Wu, Timothy Haoning, Wu, Haoning, Yang, Biao, Yang, Fuzhi, Yoo, Jaejun, Zhao, Tongtong, Zhou, Yuanbo, Zhuo, Haijie, Zong, Ziyao, and Zou, Xueyi

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided along with a set of unpaired high-quality target images. In Track 1: Image Processing artifacts, the aim is to super-resolve images with synthetically generated image processing artifacts. This allows for quantitative benchmarking of the approaches \wrt a ground-truth image. In Track 2: Smartphone Images, real low-quality smart phone images have to be super-resolved. In both tracks, the ultimate goal is to achieve the best perceptual quality, evaluated using a human study. This is the second challenge on the subject, following AIM 2019, targeting to advance the state-of-the-art in super-resolution. To measure the performance we use the benchmark protocol from AIM 2019. In total 22 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem.

Published
2020

9. MGBPv2: Scaling Up Multi-Grid Back-Projection Networks

Author

Michelini, Pablo Navarrete, Chen, Wenbin, Liu, Hanwen, and Zhu, Dan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Here, we describe our solution for the AIM-2019 Extreme Super-Resolution Challenge, where we won the 1st place in terms of perceptual quality (MOS) similar to the ground truth and achieved the 5th place in terms of high-fidelity (PSNR). To tackle this challenge, we introduce the second generation of MultiGrid BackProjection networks (MGBPv2) whose major modifications make the system scalable and more general than its predecessor. It combines the scalability of the multigrid algorithm and the performance of iterative backprojections. In its original form, MGBP is limited to a small number of parameters due to a strongly recursive structure. In MGBPv2, we make full use of the multigrid recursion from the beginning of the network; we allow different parameters in every module of the network; we simplify the main modules; and finally, we allow adjustments of the number of network features based on the scale of operation. For inference tasks, we introduce an overlapping patch approach to further allow processing of very large images (e.g. 8K). Our training strategies make use of a multiscale loss, combining distortion and/or perception losses on the output as well as downscaled output images. The final system can balance between high quality and high performance., Comment: In ICCV 2019 Workshops. Winner of Perceptual track in AIM Extreme Super-Resolution Challenge 2019. Code available at https://github.com/pnavarre/mgbpv2

Published
2019

10. A Tour of Convolutional Networks Guided by Linear Interpreters

Author

Michelini, Pablo Navarrete, Liu, Hanwen, Lu, Yunhua, and Jiang, Xingqun

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Mathematics - Numerical Analysis
Abstract

Convolutional networks are large linear systems divided into layers and connected by non-linear units. These units are the "articulations" that allow the network to adapt to the input. To understand how a network manages to solve a problem we must look at the articulated decisions in entirety. If we could capture the actions of non-linear units for a particular input, we would be able to replay the whole system back and forth as if it was always linear. It would also reveal the actions of non-linearities because the resulting linear system, a Linear Interpreter, depends on the input image. We introduce a hooking layer, called a LinearScope, which allows us to run the network and the linear interpreter in parallel. Its implementation is simple, flexible and efficient. From here we can make many curious inquiries: how do these linear systems look like? When the rows and columns of the transformation matrix are images, how do they look like? What type of basis do these linear transformations rely on? The answers depend on the problems presented, through which we take a tour to some popular architectures used for classification, super-resolution (SR) and image-to-image translation (I2I). For classification we observe that popular networks use a pixel-wise vote per class strategy and heavily rely on bias parameters. For SR and I2I we find that CNNs use wavelet-type basis similar to the human visual system. For I2I we reveal copy-move and template-creation strategies to generate outputs., Comment: To appear in ICCV 2019

Published
2019

11. PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report

Author

Ignatov, Andrey, Timofte, Radu, Van Vu, Thang, Luu, Tung Minh, Pham, Trung X, Van Nguyen, Cao, Kim, Yongwoo, Choi, Jae-Seok, Kim, Munchurl, Huang, Jie, Ran, Jiewen, Xing, Chen, Zhou, Xingguang, Zhu, Pengfei, Geng, Mingrui, Li, Yawei, Agustsson, Eirikur, Gu, Shuhang, Van Gool, Luc, de Stoutz, Etienne, Kobyshev, Nikolay, Nie, Kehui, Zhao, Yan, Li, Gen, Tong, Tong, Gao, Qinquan, Hanwen, Liu, Michelini, Pablo Navarrete, Dan, Zhu, Fengshuo, Hu, Hui, Zheng, Wang, Xiumei, Deng, Lirui, Meng, Rang, Qin, Jinghui, Shi, Yukai, Wen, Wushao, Lin, Liang, Feng, Ruicheng, Wu, Shixiang, Dong, Chao, Qiao, Yu, Vasu, Subeesh, Madam, Nimisha Thekke, Kandula, Praveen, Rajagopalan, A. N., Liu, Jie, and Jung, Cheolkon

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

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 tracks. In the first one, participants were solving the classical image super-resolution problem with a bicubic downscaling factor of 4. The second track was aimed at real-world photo enhancement, and the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with a DSLR camera. The target metric used in this challenge combined the runtime, PSNR scores and solutions' perceptual results measured in the user study. To ensure the efficiency of the submitted models, we additionally measured their runtime and memory requirements on Android smartphones. The proposed solutions significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.

Published
2018

12. Multi-Scale Recursive and Perception-Distortion Controllable Image Super-Resolution

Author

Michelini, Pablo Navarrete, Zhu, Dan, and Liu, Hanwen

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

We describe our solution for the PIRM Super-Resolution Challenge 2018 where we achieved the 2nd best perceptual quality for average RMSE<=16, 5th best for RMSE<=12.5, and 7th best for RMSE<=11.5. We modify a recently proposed Multi-Grid Back-Projection (MGBP) architecture to work as a generative system with an input parameter that can control the amount of artificial details in the output. We propose a discriminator for adversarial training with the following novel properties: it is multi-scale that resembles a progressive-GAN; it is recursive that balances the architecture of the generator; and it includes a new layer to capture significant statistics of natural images. Finally, we propose a training strategy that avoids conflicts between reconstruction and perceptual losses. Our configuration uses only 281k parameters and upscales each image of the competition in 0.2s in average., Comment: In ECCV 2018 Workshops. Won 2nd place in Region 3 of PIRM-SR Challenge 2018. Code and models are available at https://github.com/pnavarre/pirm-sr-2018

Published
2018

13. Multigrid Backprojection Super-Resolution and Deep Filter Visualization

Author

Michelini, Pablo Navarrete, Liu, Hanwen, and Zhu, Dan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

We introduce a novel deep-learning architecture for image upscaling by large factors (e.g. 4x, 8x) based on examples of pristine high-resolution images. Our target is to reconstruct high-resolution images from their downscale versions. The proposed system performs a multi-level progressive upscaling, starting from small factors (2x) and updating for higher factors (4x and 8x). The system is recursive as it repeats the same procedure at each level. It is also residual since we use the network to update the outputs of a classic upscaler. The network residuals are improved by Iterative Back-Projections (IBP) computed in the features of a convolutional network. To work in multiple levels we extend the standard back-projection algorithm using a recursion analogous to Multi-Grid algorithms commonly used as solvers of large systems of linear equations. We finally show how the network can be interpreted as a standard upsampling-and-filter upscaler with a space-variant filter that adapts to the geometry. This approach allows us to visualize how the network learns to upscale. Finally, our system reaches state of the art quality for models with relatively few number of parameters., Comment: Spotlight paper in the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)

Published
2018

14. Convolutional Networks with MuxOut Layers as Multi-rate Systems for Image Upscaling

Author

Michelini, Pablo Navarrete and Liu, Hanwen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We interpret convolutional networks as adaptive filters and combine them with so-called MuxOut layers to efficiently upscale low resolution images. We formalize this interpretation by deriving a linear and space-variant structure of a convolutional network when its activations are fixed. We introduce general purpose algorithms to analyze a network and show its overall filter effect for each given location. We use this analysis to evaluate two types of image upscalers: deterministic upscalers that target the recovery of details from original content; and second, a new generation of upscalers that can sample the distribution of upscale aliases (images that share the same downscale version) that look like real content.

Published
2017

15. PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report

Author

Ignatov, Andrey, Timofte, Radu, Van Vu, Thang, Luu, Tung Minh, Pham, Trung X, Van Nguyen, Cao, Kim, Yongwoo, Choi, Jae-Seok, Kim, Munchurl, Huang, Jie, Ran, Jiewen, Xing, Chen, Zhou, Xingguang, Zhu, Pengfei, Geng, Mingrui, Li, Yawei, Agustsson, Eirikur, Gu, Shuhang, Van Gool, Luc, de Stoutz, Etienne, Kobyshev, Nikolay, Nie, Kehui, Zhao, Yan, Li, Gen, Tong, Tong, Gao, Qinquan, Hanwen, Liu, Michelini, Pablo Navarrete, Dan, Zhu, Fengshuo, Hu, Hui, Zheng, Wang, Xiumei, Deng, Lirui, Meng, Rang, Qin, Jinghui, Shi, Yukai, Wen, Wushao, Lin, Liang, Feng, Ruicheng, Wu, Shixiang, Dong, Chao, Qiao, Yu, Vasu, Subeesh, Thekke Madam, Nimisha, Kandula, Praveen, Rajagopalan, A. N., Liu, Jie, Jung, Cheolkon, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Leal-Taixé, Laura, editor, and Roth, Stefan, editor

Published
2019
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16. Direct Multi-grid Methods for Linear Systems with Harmonic Aliasing Patterns

Author

Michelini, Pablo Navarrete

Subjects
Mathematics - Numerical Analysis, 65N55, 65F05, 94A12, 65T99
Abstract

Multi-level numerical methods that obtain the exact solution of a linear system are presented. The methods are devised by combining ideas from the full multi-grid algorithm and perfect reconstruction filters. The problem is stated as whether a direct solver is possible in a full multi-grid scheme by avoiding smoothing iterations and using different coarse grids at each step. The coarse grids must form a partition of the fine grid and thus establishes a strong connection with domain decomposition methods. An important analogy is established between the conditions for direct solution in multi-grid solvers and perfect reconstruction in filter banks. Furthermore, simple solutions of these conditions for direct multi-grid solvers are found by using mirror filters. As a result, different configurations of direct multi-grid solvers are obtained and studied.

Published
2009
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17. NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

Author

Yang, Ren, primary, Timofte, Radu, additional, Zheng, Meisong, additional, Xing, Qunliang, additional, Qiao, Minglang, additional, Xu, Mai, additional, Jiang, Lai, additional, Liu, Huaida, additional, Chen, Ying, additional, Ben, Youcheng, additional, Zhou, Xiao, additional, Fu, Chen, additional, Cheng, Pei, additional, Yu, Gang, additional, Li, Junyi, additional, Wu, Renlong, additional, Zhang, Zhilu, additional, Shang, Wei, additional, Lv, Zhengyao, additional, Chen, Yunjin, additional, Zhou, Mingcai, additional, Ren, Dongwei, additional, Zhang, Kai, additional, Zuo, Wangmeng, additional, Ostyakov, Pavel, additional, Dmitry, Vyal, additional, Soltanayev, Shakarim, additional, Sergey, Chervontsev, additional, Magauiya, Zhussip, additional, Zou, Xueyi, additional, Yan, Youliang, additional, Michelini, Pablo Navarrete, additional, Lu, Yunhua, additional, Zhang, Diankai, additional, Liu, Shaoli, additional, Gao, Si, additional, Wu, Biao, additional, Zheng, Chengjian, additional, Zhang, Xiaofeng, additional, Lu, Kaidi, additional, Wang, Ning, additional, Canh, Thuong Nguyen, additional, Bach, Thong, additional, Wang, Qing, additional, Sun, Xiaopeng, additional, Ma, Haoyu, additional, Zhao, Shijie, additional, Li, Junlin, additional, Xie, Liangbin, additional, Shi, Shuwei, additional, Yang, Yujiu, additional, Wang, Xintao, additional, Gu, Jinjin, additional, Dong, Chao, additional, Shi, Xiaodi, additional, Nian, Chunmei, additional, Jiang, Dong, additional, Lin, Jucai, additional, Xie, Zhihuai, additional, Ye, Mao, additional, Luo, Dengyan, additional, Peng, Liuhan, additional, Chen, Shengjie, additional, Liu, Xin, additional, Wang, Qian, additional, Liang, Boyang, additional, Dong, Hang, additional, Huang, Yuhao, additional, Chen, Kai, additional, Guo, Xingbei, additional, Sun, Yujing, additional, Wu, Huilei, additional, Wei, Pengxu, additional, Huang, Yulin, additional, Chen, Junying, additional, Hyun Lee, Ik, additional, Ali Khowaja, Sunder, additional, and Yoon, Jiseok, additional

Published
2022
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21. NTIRE 2021 Challenge on Video Super-Resolution

Author

Son, Sanghyun, primary, Lee, Suyoung, additional, Nah, Seungjun, additional, Timofte, Radu, additional, Lee, Kyoung Mu, additional, Chan, Kelvin C. K., additional, Zhou, Shangchen, additional, Xu, Xiangyu, additional, Loy, Chen Change, additional, Jiang, Boyuan, additional, Lin, Chuming, additional, Dong, Yuchun, additional, Luo, Donghao, additional, Chu, Wenqing, additional, Ji, Xiaozhong, additional, Yang, Siqian, additional, Tai, Ying, additional, Wang, Chengjie, additional, Li, Jilin, additional, Huang, Feiyue, additional, Chen, Chengpeng, additional, Chu, Xiaojie, additional, Zhang, Jie, additional, Lu, Xin, additional, Chen, Liangyu, additional, Lin, Jing, additional, Du, Guodong, additional, Hao, Jia, additional, Zou, Xueyi, additional, Zhang, Qi, additional, Jiang, Lielin, additional, Li, Xin, additional, Zheng, He, additional, Liu, Fanglong, additional, He, Dongliang, additional, Li, Fu, additional, Dang, Qingqing, additional, Yi, Peng, additional, Wang, Zhongyuan, additional, Jiang, Kui, additional, Jiang, Junjun, additional, Ma, Jiayi, additional, Chen, Yuxiang, additional, Wang, Yutong, additional, Liu, Ting, additional, Sun, Qichao, additional, Liang, Huanwei, additional, Li, Yiming, additional, Li, Zekun, additional, Ruan, Zhubo, additional, Shang, Fanjie, additional, Guo, Chen, additional, Li, Haining, additional, Luo, Renjun, additional, Shen, Longjie, additional, Zafirouli, Kassiani, additional, Karageorgos, Konstantinos, additional, Konstantoudakis, Konstantinos, additional, Dimou, Anastasios, additional, Daras, Petros, additional, Song, Xiaowei, additional, Zhuo, Xu, additional, Liu, Hanxi, additional, Guo, Mengxi, additional, Li, Junlin, additional, Li, Yu, additional, Zhu, Ye, additional, Wang, Qing, additional, Zhao, Shijie, additional, Sun, Xiaopeng, additional, Zhan, Gen, additional, Xie, Tangxin, additional, Jia, Yu, additional, Lu, Yunhua, additional, Zhang, Wenhao, additional, Sun, Mengdi, additional, Michelini, Pablo Navarrete, additional, Zhang, Xueheng, additional, Jiang, Hao, additional, Chen, Zhiyu, additional, Chen, Li, additional, Xiong, Zhiwei, additional, Xiao, Zeyu, additional, Xu, Ruikang, additional, Cheng, Zhen, additional, Fu, Xueyang, additional, Song, Fenglong, additional, Luo, Zhipeng, additional, Yao, Yuehan, additional, Dutta, Saikat, additional, Shah, Nisarg A., additional, Dipta Das, Sourya, additional, Zhao, Peng, additional, Shi, Yukai, additional, Liu, Hongying, additional, Shang, Fanhua, additional, Liu, Yuanyuan, additional, Chen, Fei, additional, Yu, Fangxu, additional, Gao, Ruisheng, additional, Bai, Yixin, additional, Heo, Jeonghwan, additional, Yue, Shijie, additional, Li, Chenghua, additional, Li, Jinjing, additional, Zheng, Qian, additional, Gang, Ruipeng, additional, Song, Ruixia, additional, Wee, Seungwoo, additional, Jeong, Jechang, additional, Li, Chen, additional, Wen, Geyingjie, additional, Chai, Xinning, additional, and Song, Li, additional

Published
2021
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22. NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

Author

Yang, Ren, primary, Timofte, Radu, additional, Liu, Jing, additional, Xu, Yi, additional, Zhang, Xinjian, additional, Zhao, Minyi, additional, Zhou, Shuigeng, additional, Chan, Kelvin C.K., additional, Zhou, Shangchen, additional, Xu, Xiangyu, additional, Loy, Chen Change, additional, Li, Xin, additional, Liu, Fanglong, additional, Zheng, He, additional, Jiang, Lielin, additional, Zhang, Qi, additional, He, Dongliang, additional, Li, Fu, additional, Dang, Qingqing, additional, Huang, Yibin, additional, Maggioni, Matteo, additional, Fu, Zhongqian, additional, Xiao, Shuai, additional, Li, Cheng, additional, Tanay, Thomas, additional, Song, Fenglong, additional, Chao, Wentao, additional, Guo, Qiang, additional, Liu, Yan, additional, Li, Jiang, additional, Qu, Xiaochao, additional, Hou, Dewang, additional, Yang, Jiayu, additional, Jiang, Lyn, additional, You, Di, additional, Zhang, Zhenyu, additional, Mou, Chong, additional, Koshelev, Iaroslav, additional, Ostyakov, Pavel, additional, Somov, Andrey, additional, Hao, Jia, additional, Zou, Xueyi, additional, Zhao, Shijie, additional, Sun, Xiaopeng, additional, Liao, Yiting, additional, Zhang, Yuanzhi, additional, Wang, Qing, additional, Zhan, Gen, additional, Guo, Mengxi, additional, Li, Junlin, additional, Lu, Ming, additional, Ma, Zhan, additional, Michelini, Pablo Navarrete, additional, Wang, Hai, additional, Chen, Yiyun, additional, Guo, Jingyu, additional, Zhang, Liliang, additional, Yang, Wenming, additional, Kim, Sijung, additional, Oh, Syehoon, additional, Wang, Yucong, additional, Cai, Minjie, additional, Hao, Wei, additional, Shi, Kangdi, additional, Li, Liangyan, additional, Chen, Jun, additional, Gao, Wei, additional, Liu, Wang, additional, Zhang, Xiaoyu, additional, Zhou, Linjie, additional, Lin, Sixin, additional, and Wang, Ru, additional

Published
2021
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25. NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

Author

Abdelhamed, Abdelrahman, primary, Afifi, Mahmoud, additional, Timofte, Radu, additional, Brown, Michael S., additional, Cao, Yue, additional, Zhang, Zhilu, additional, Zuo, Wangmeng, additional, Zhang, Xiaoling, additional, Liu, Jiye, additional, Chen, Wendong, additional, Wen, Changyuan, additional, Liu, Meng, additional, Lv, Shuailin, additional, Zhang, Yunchao, additional, Pan, Zhihong, additional, Li, Baopu, additional, Xi, Teng, additional, Fan, Yanwen, additional, Yu, Xiyu, additional, Zhang, Gang, additional, Liu, Jingtuo, additional, Han, Junyu, additional, Ding, Errui, additional, Yu, Songhyun, additional, Park, Bumjun, additional, Jeong, Jechang, additional, Liu, Shuai, additional, Zong, Ziyao, additional, Nan, Nan, additional, Li, Chenghua, additional, Yang, Zengli, additional, Bao, Long, additional, Wang, Shuangquan, additional, Bai, Dongwoon, additional, Lee, Jungwon, additional, Kim, Youngjung, additional, Rho, Kyeongha, additional, Shin, Changyeop, additional, Kim, Sungho, additional, Tang, Pengliang, additional, Zhao, Yiyun, additional, Zhou, Yuqian, additional, Fan, Yuchen, additional, Huang, Thomas, additional, Li, Zhihao, additional, Shah, Nisarg A., additional, Liu, Wei, additional, Yan, Qiong, additional, Zhao, Yuzhi, additional, Mozejko, Marcin, additional, Latkowski, Tomasz, additional, Treszczotko, Lukasz, additional, Szafraniuk, Michal, additional, Trojanowski, Krzysztof, additional, Wu, Yanhong, additional, Michelini, Pablo Navarrete, additional, Hu, Fengshuo, additional, Lu, Yunhua, additional, Kim, Sujin, additional, Kim, Wonjin, additional, Lee, Jaayeon, additional, Choi, Jang-Hwan, additional, Zhussip, Magauiya, additional, Khassenov, Azamat, additional, Kim, Jong Hyun, additional, Cho, Hwechul, additional, Kansal, Priya, additional, Nathan, Sabari, additional, Ye, Zhangyu, additional, Lu, Xiwen, additional, Wu, Yaqi, additional, Yang, Jiangxin, additional, Cao, Yanlong, additional, Tang, Siliang, additional, Cao, Yanpeng, additional, Maggioni, Matteo, additional, Marras, Ioannis, additional, Tanay, Thomas, additional, Slabaugh, Gregory, additional, Yan, Youliang, additional, Kang, Myungjoo, additional, Choi, Han-Soo, additional, Song, Kyungmin, additional, Xu, Shusong, additional, Lu, Xiaomu, additional, Wang, Tingniao, additional, Lei, Chunxia, additional, Liu, Bin, additional, Gupta, Rajat, additional, and Kumar, Vineet, additional

Published
2020
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26. NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results

Author

Lugmayr, Andreas, primary, Danelljan, Martin, additional, Timofte, Radu, additional, Ahn, Namhyuk, additional, Bai, Dongwoon, additional, Cai, Jie, additional, Cao, Yun, additional, Chen, Junyang, additional, Cheng, Kaihua, additional, Chun, SeYoung, additional, Deng, Wei, additional, El-Khamy, Mostafa, additional, Ho, Chiu Man, additional, Ji, Xiaozhong, additional, Kheradmand, Amin, additional, Kim, Gwantae, additional, Ko, Hanseok, additional, Lee, Kanghyu, additional, Lee, Jungwon, additional, Li, Hao, additional, Liu, Ziluan, additional, Liu, Zhi-Song, additional, Liu, Shuai, additional, Lu, Yunhua, additional, Meng, Zibo, additional, Michelini, Pablo Navarrete, additional, Micheloni, Christian, additional, Prajapati, Kalpesh, additional, Ren, Haoyu, additional, Seo, Yong Hyeok, additional, Siu, Wan-Chi, additional, Sohn, Kyung-Ah, additional, Tai, Ying, additional, Umer, Rao Muhammad, additional, Wang, Shuangquan, additional, Wang, Huibing, additional, Wu, Timothy Haoning, additional, Wu, Haoning, additional, Yang, Biao, additional, Yang, Fuzhi, additional, Yoo, Jaejun, additional, Zhao, Tongtong, additional, Zhou, Yuanbo, additional, Zhuo, Haijie, additional, Zong, Ziyao, additional, and Zou, Xueyi, additional

Published
2020
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30. NTIRE 2019 Image Dehazing Challenge Report

Author

Ancuti, Codruta O., primary, Ancuti, Cosmin, additional, Timofte, Radu, additional, Van Gool, Luc, additional, Zhang, Lei, additional, Yang, Ming-Hsuan, additional, Guo, Tiantong, additional, Li, Xuelu, additional, Cherukuri, Venkateswararao, additional, Monga, Vishal, additional, Jiang, Hao, additional, Yang, Siyuan, additional, Liu, Yan, additional, Qu, Xiaochao, additional, Wan, Pengfei, additional, Park, Dongwon, additional, Chun, Se Young, additional, Hong, Ming, additional, Huang, Jinying, additional, Chen, Yizi, additional, Chen, Shuxin, additional, Wang, Bomin, additional, Michelini, Pablo Navarrete, additional, Liu, Hanwen, additional, Zhu, Dan, additional, Liu, Jing, additional, Santra, Sanchayan, additional, Mondal, Ranjan, additional, Chanda, Bhabatosh, additional, Morales, Peter, additional, Klinghoffer, Tzofi, additional, Quan, Le Manh, additional, Kim, Yong-Guk, additional, Liang, Xiao, additional, Li, Runde, additional, Pan, Jinshan, additional, Tang, Jinhui, additional, Purohit, Kuldeep, additional, Suin, Maitreya, additional, Rajagopalan, A.N., additional, Schettini, Raimondo, additional, Bianco, Simone, additional, Piccoli, Flavio, additional, Cusano, C., additional, Celona, Luigi, additional, Hwang, Sunhee, additional, Ma, Yu Seung, additional, Byun, Hyeran, additional, Murala, Subrahmanyam, additional, Dudhane, Akshay, additional, Aulakh, Harsh, additional, Zheng, Tianxiang, additional, Zhang, Tao, additional, Qin, Weining, additional, Zhou, Runnan, additional, Wang, Shanhu, additional, Tarel, Jean-Philippe, additional, Wang, Chuansheng, additional, and Wu, Jiawei, additional

Published
2019
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31. NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results

Author

Cai, Jianrui, primary, Gu, Shuhang, additional, Timofte, Radu, additional, Zhang, Lei, additional, Liu, Xiao, additional, Ding, Yukang, additional, He, Dongliang, additional, Li, Chao, additional, Fu, Yi, additional, Wen, Shilei, additional, Feng, Ruicheng, additional, Gu, Jinjin, additional, Qiao, Yu, additional, Dong, Chao, additional, Park, Dongwon, additional, Chun, Se Young, additional, Yoon, Sanghoon, additional, Kwak, Junhyung, additional, Son, Donghee, additional, Zamir, Syed Waqas, additional, Arora, Aditya, additional, Khan, Salman, additional, Khan, Fahad Shahbaz, additional, Shao, Ling, additional, Wei, Zhengping, additional, Liu, Lei, additional, Cai, Hong, additional, Li, Darui, additional, Gao, Fujie, additional, Hui, Zheng, additional, Wang, Xiumei, additional, Gao, Xinbo, additional, Cheng, Guoan, additional, Matsune, Ai, additional, Li, Qiuyu, additional, Zhu, Leilei, additional, Zang, Huaijuan, additional, Zhan, Shu, additional, Qiu, Yajun, additional, wang, Ruxin, additional, Li, Jiawei, additional, Jing, Yongcheng, additional, Song, Mingli, additional, Liu, Pengju, additional, Zhang, Kai, additional, Liu, Jingdong, additional, Liu, Jiye, additional, Zhang, Hongzhi, additional, Zuo, Wangmeng, additional, Tang, Wenyi, additional, Liu, Jing, additional, Kim, Youngjung, additional, Shin, Changyeop, additional, Kim, Minbeom, additional, Kim, Sungho, additional, Michelini, Pablo Navarrete, additional, Liu, Hanwen, additional, Zhu, Dan, additional, Xu, Xuan, additional, Li, Xin, additional, Bai, Furui, additional, Sun, Xiaopeng, additional, Zha, Lin, additional, Huang, Yuanfei, additional, Lu, Wen, additional, Cao, Yanpeng, additional, Chen, Du, additional, He, Zewei, additional, Anshun, Sun, additional, Tang, Siliang, additional, Fan, Hongfei, additional, Li, Xiang, additional, Li, Guo, additional, Zhang, Wenjie, additional, Zhang, Yumei, additional, He, Qingwen, additional, Qin, Jinghui, additional, Huang, Lishan, additional, Shi, Yukai, additional, Wei, Pengxu, additional, Wen, Wushao, additional, Lin, Liang, additional, Yu, Jun, additional, Xie, Guochen, additional, Li, Mengyan, additional, Chen, Rong, additional, Luo, Xiaotong, additional, Hong, Chen, additional, Qu, Yanyun, additional, Li, Cuihua, additional, Liu, Zhi-Song, additional, Wang, Li-Wen, additional, Li, Chu-Tak, additional, Zhao, Can, additional, Li, Bowen, additional, Tsai, Chung-Chi, additional, Chuang, Shang-Chih, additional, Choi, Joon-Hee, additional, Kim, Joonsoo, additional, Jiang, Xiaoyun, additional, Pan, Ze, additional, Lv, Qunbo, additional, Tan, Zheng, additional, and He, Peidong, additional

Published
2019
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32. NTIRE 2019 Challenge on Image Enhancement: Methods and Results

Author

Ignatov, Andrey, primary, Timofte, Radu, additional, Qu, Xiaochao, additional, Zhou, Xingguang, additional, Liu, Ting, additional, Wan, Pengfei, additional, Zamir, Syed Waqas, additional, Arora, Aditya, additional, Khan, Salman, additional, Khan, Fahad Shahbaz, additional, Shao, Ling, additional, Park, Dongwon, additional, Chun, Se Young, additional, Michelini, Pablo Navarrete, additional, Liu, Hanwen, additional, Zhu, Dan, additional, Zhong, Zhiwei, additional, Liu, Xianming, additional, Jiang, Junjun, additional, Zhao, Debin, additional, Haris, Muhammad, additional, Akita, Kazutoshi, additional, Yoshida, Tomoki, additional, Shakhnarovich, Greg, additional, Ukita, Norimichi, additional, Liu, Jie, additional, Jung, Cheolkon, additional, Schettini, Raimondo, additional, Bianco, Simone, additional, Cusano, Claudio, additional, Piccoli, Flavio, additional, Liu, Pengju, additional, Zhang, Kai, additional, Liu, Jingdong, additional, Liu, Jiye, additional, Zhang, Hongzhi, additional, Zuo, Wangmeng, additional, Bow, Nelson Chong Ngee, additional, Wong, Lai-Kuan, additional, See, John, additional, Qin, Jinghui, additional, Huang, Lishan, additional, Shi, Yukai, additional, Wei, Pengxu, additional, Wen, Wushao, additional, Lin, Liang, additional, Hui, Zheng, additional, Wang, Xiumei, additional, Gao, Xinbo, additional, Kumari, Kanti, additional, Anand, Vikas Kumar, additional, Khened, Mahendra, additional, and Krishnamurthi, Ganapathy, additional

Published
2019
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35. NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

Author

Timofte, Radu, primary, Gu, Shuhang, additional, Wu, Jiqing, additional, Van Gool, Luc, additional, Zhang, Lei, additional, Yang, Ming-Hsuan, additional, Haris, Muhammad, additional, Shakhnarovich, Greg, additional, Ukita, Norimichi, additional, Hu, Shijia, additional, Bei, Yijie, additional, Hui, Zheng, additional, Jiang, Xiao, additional, Gu, Yanan, additional, Liu, Jie, additional, Wang, Yifan, additional, Perazzi, Federico, additional, McWilliams, Brian, additional, Sorkine-Hornung, Alexander, additional, Sorkine-Hornung, Olga, additional, Schroers, Christopher, additional, Yu, Jiahui, additional, Fan, Yuchen, additional, Yang, Jianchao, additional, Xu, Ning, additional, Wang, Zhaowen, additional, Wang, Xinchao, additional, Huang, Thomas S., additional, Wang, Xintao, additional, Yu, Ke, additional, Hui, Tak-Wai, additional, Dong, Chao, additional, Lin, Liang, additional, Change Loy, Chen, additional, Park, Dongwon, additional, Kim, Kwanyoung, additional, Chun, Se Young, additional, Zhang, Kai, additional, Liu, Pengjv, additional, Zuo, Wangmeng, additional, Guo, Shi, additional, Liu, Jiye, additional, Xu, Jinchang, additional, Liu, Yijiao, additional, Xiong, Fengye, additional, Dong, Yuan, additional, Bai, Hongliang, additional, Damian, Alexandru, additional, Ravi, Nikhil, additional, Menon, Sachit, additional, Rudin, Cynthia, additional, Seo, Junghoon, additional, Jeon, Taegyun, additional, Koo, Jamyoung, additional, Jeon, Seunghyun, additional, Kim, Soo Ye, additional, Choi, Jae-Seok, additional, Ki, Sehwan, additional, Seo, Soomin, additional, Sim, Hyeonjun, additional, Kim, Saehun, additional, Kim, Munchurl, additional, Chen, Rong, additional, Zeng, Kun, additional, Guo, Jinkang, additional, Qu, Yanyun, additional, Li, Cuihua, additional, Ahn, Namhyuk, additional, Kang, Byungkon, additional, Sohn, Kyung-Ah, additional, Yuan, Yuan, additional, Zhang, Jiawei, additional, Pang, Jiahao, additional, Xu, Xiangyu, additional, Zhao, Yan, additional, Deng, Wei, additional, Ul Hussain, Sibt, additional, Aadil, Muneeb, additional, Rahim, Rafia, additional, Cai, Xiaowang, additional, Huang, Fang, additional, Xu, Yueshu, additional, Michelini, Pablo Navarrete, additional, Zhu, Dan, additional, Liu, Hanwen, additional, Kim, Jun-Hyuk, additional, Lee, Jong-Seok, additional, Huang, Yiwen, additional, Qiu, Ming, additional, Jing, Liting, additional, Zeng, Jiehang, additional, Wang, Ying, additional, Sharma, Manoj, additional, Mukhopadhyay, Rudrabha, additional, Upadhyay, Avinash, additional, Koundinya, Sriharsha, additional, Shukla, Ankit, additional, Chaudhury, Santanu, additional, Zhang, Zhe, additional, Hu, Yu Hen, additional, and Fu, Lingzhi, additional

Published
2018
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40. 10K Ultra-High-Definition Display System.

Author

Ran, Duan and Michelini, Pablo Navarrete

Subjects
DIGITAL image processing, HIGH Definition Multimedia Interface, DISPLAY systems
Abstract

Our 82' 10K display panel shown in SID 2015 only supported a 10K input signal. We show our upgraded 10K system that now includes several processing and enhancement modules. By integrating SOC and FPGA technologies, we are now able to provide a powerful multi-input and unique UHD system. [ABSTRACT FROM AUTHOR]

Published
2016
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