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NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results [article]

Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte, Michael S. Brown, Yue Cao, Zhilu Zhang, Wangmeng Zuo, Xiaoling Zhang, Jiye Liu, Wendong Chen, Changyuan Wen, Meng Liu (+78 others)
2020 arXiv   pre-print
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.  ...  Acknowledgements We thank the NTIRE 2020 sponsors: Huawei, Oppo, Voyage81, MediaTek, DisneyResearch|Studios, and Computer Vision Lab (CVL) ETH Zurich. A. Teams and  ... 
arXiv:2005.04117v1 fatcat:iwtpyxikerbqhhvkpmwghqxeke

NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results [article]

Andreas Lugmayr, Martin Danelljan, Radu Timofte, Namhyuk Ahn, Dongwoon Bai, Jie Cai, Yun Cao, Junyang Chen, Kaihua Cheng, SeYoung Chun, Wei Deng, Mostafa El-Khamy (+34 others)
2020 arXiv   pre-print
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.  ...  Acknowledgements We thank the NTIRE 2020 sponsors: Huawei, Oppo, Voyage81, MediaTek, DisneyResearch|Studios, and Computer Vision Lab (CVL) ETH Zurich.  ... 
arXiv:2005.01996v1 fatcat:ewngd7chdve3fbvwis32v64ruq

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results [article]

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 (+51 others)
2020 arXiv   pre-print
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.  ...  The challenge task was to super-resolve an input image with a magnification factor 16 based on a set of prior examples of low and corresponding high resolution images.  ...  Acknowledgements We thank the NTIRE 2020 sponsors: HUAWEI, OPPO, Voyage81, MediaTek, DisneyResearch|Studios, and Computer Vision Lab (CVL) ETH Zurich.  ... 
arXiv:2005.01056v1 fatcat:6nwj5ilbgbgjnmd6oy435hjdhi

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation [article]

Jiaming Liu, Chi-Hao Wu, Yuzhi Wang, Qin Xu, Yuqian Zhou, Haibin Huang, Chuan Wang, Shaofan Cai, Yifan Ding, Haoqiang Fan, Jue Wang
2019 arXiv   pre-print
Combining these data processing technqiues with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969 in NTIRE 2019 Real Image Denoising Challenge, demonstrating the state-of-the-art  ...  Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to effectively handle various Bayer patterns from different data  ...  We apply these techniques to train models based on our modified U-Net [25] , and achieve state-of-the-art results in NTIRE 2019 Real Image Denoising Challenge (Track 1) [2] .  ... 
arXiv:1904.12945v2 fatcat:xixhjw4yrzcxzmzx3xohytnihu

Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising [article]

Yali Peng, Yue Cao, Shigang Liu, Jian Yang, Wangmeng Zuo
2020 arXiv   pre-print
Experiments on both synthetic and real-world noisy images show that our PT-MWRN performs favorably against the state-of-the-art denoising methods in terms both quantitative metrics and visual quality.  ...  to denoising results.  ...  In NTIRE 2019 Challenge on Real Image Denoising [46] , GRDN [47] , DHDN [24] and DIDN [48] have won the first three places on the sRGB track.  ... 
arXiv:2010.12422v1 fatcat:3ojm6hu6c5c3xotsawomiz6nri

Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenes [article]

Huanjing Yue, Cong Cao, Lei Liao, Ronghe Chu, Jingyu Yang
2020 arXiv   pre-print
Experimental results demonstrate that our method outperforms state-of-the-art video and raw image denoising algorithms on both indoor and outdoor videos.  ...  In recent years, the supervised learning strategy for real noisy image denoising has been emerging and has achieved promising results.  ...  The winner of NTIRE 2019 Real Image Denoising Challenge proposed a Bayer preserving augmentation method for raw image denoising, and achieved state-of-the-art denoising results [23] .  ... 
arXiv:2003.14013v1 fatcat:ksp64aeqovdyvk5gaipdeixv2u

DRNet: A Deep Neural Network With Multi-layer Residual Blocks Improves Image Denoising

Jiahong Zhang, Yonggui Zhu, Wenyi Li, Wenlong Fu, Lihong Cao
2021 IEEE Access  
For real image denoising, we use training images released by the NTIRE 2020 Real Image Denoising Challenge-Track2: sRGB, which are from the SIDD dataset [46] .  ...  FIGURE 5 . 5 Denoising results on the image Monarch from the Set12 with different noise level FIGURE 6 . 6 Denoising results of different methods on one image from the Kodak24 with σ = 50: (a) noisy  ... 
doi:10.1109/access.2021.3084951 fatcat:atxek5cihbhwbiihcv5uytj2eq

SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution [article]

Namhyuk Ahn and Jaejun Yoo and Kyung-Ah Sohn
2020 arXiv   pre-print
We submitted our method in NTIRE 2020 super-resolution challenge and won 1st in PSNR, 2nd in SSIM, and 13th in LPIPS.  ...  By allowing multiple LR images, we build a set of pseudo pairs by denoising and downsampling LR images and cast the original unsupervised problem into a supervised learning problem but in one level lower  ...  This work was supported by NAVER Corporation and also by the National Research Foundation of Korea grant funded by the Korea government (MSIT) (no.NRF-2019R1A2C1006608)  ... 
arXiv:2004.11020v1 fatcat:4r5xru6zzbdz3ezgucrj5yzofi

Unsupervised Image Super-Resolution with an Indirect Supervised Path [article]

Zhen Han, Enyan Dai, Xu Jia, Xiaoying Ren, Shuaijun Chen, Chunjing Xu, Jianzhuang Liu, Qi Tian
2019 arXiv   pre-print
The proposed method is evaluated on both NTIRE 2017 and 2018 challenge datasets and achieves favorable performance against supervised methods.  ...  Although significant progress has been made by deep learning models, they are trained on synthetic paired data in a supervised way and do not perform well on real data.  ...  much better visual results in terms of sharpening and denoising.  ... 
arXiv:1910.02593v2 fatcat:fzdh5sjk2nbkpc7fdgcpkvrpoq

NTIRE 2020 Challenge on Image and Video Deblurring [article]

Seungjun Nah, Sanghyun Son, Radu Timofte, Kyoung Mu Lee
2020 arXiv   pre-print
Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring.  ...  The winning methods demonstrate the state-ofthe-art performance on image and video deblurring tasks.  ...  Acknowledgments We thank the NTIRE 2020 sponsors: HUAWEI Technologies Co. Ltd., OPPO Mobile Corp., Ltd., Voyage81, MediaTek Inc., DisneyResearch|Studios, and ETH Zurich (Computer Vision Lab).  ... 
arXiv:2005.01244v2 fatcat:aoy3tyxlybefrd7yd5ywvr6jh4

NTIRE 2020 Challenge on NonHomogeneous Dehazing [article]

Codruta O. Ancuti, Cosmin Ancuti, Florin-Alexandru Vasluianu, Radu Timofte, Jing Liu, Haiyan Wu, Yuan Xie, Yanyun Qu, Lizhuang Ma, Ziling Huang, Qili Deng, Ju-Chin Chao (+40 others)
2020 arXiv   pre-print
We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor.  ...  This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images (restoration of rich details in hazy image).  ...  A second dehazing challenge has been organised by NTIRE 2019 [13] based on DenseHaze [5] , a more challenging dehazing dataset that considers dense hazy scenes with corresponding ground truth images  ... 
arXiv:2005.03457v1 fatcat:3j6klhwog5bi3powihxdlrjgeq

Generative Adversarial Networks for Image Super-Resolution: A Survey [article]

Chunwei Tian, Xuanyu Zhang, Jerry Chun-Wen Lin, Wangmeng Zuo, Yanning Zhang
2022 arXiv   pre-print
Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR.  ...  Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised  ...  2020 Real World SR challenge [173] NTIRE 2020 Real World SR challenge [173] USISResNet [129] NTIRE-2020 Real-world SR Challenge validation dataset [173] , DIV2K [151] , Flickr2k [152] , KADID  ... 
arXiv:2204.13620v1 fatcat:hlwdqith65cxrbqrnbphjz6u4u

Lightweight and Efficient Image Dehazing Network Guided by Transmission Estimation from Real-World Hazy Scenes

Zhan Li, Jianhang Zhang, Ruibin Zhong, Bir Bhanu, Yuling Chen, Qingfeng Zhang, Haoqing Tang
2021 Sensors  
dark-channel-prior (F-DCP) method from real-world hazy images as a regularizer, which facilitates network training not only on synthetic data, but also on natural images.  ...  Extensive experimental results demonstrate that, in terms of several reference and non-reference evaluation criteria for real-world images, the proposed method can achieve state-of-the-art performance  ...  Data Availability Statement: The proposed TGL-Net and some testing images can be downloaded from the link  ... 
doi:10.3390/s21030960 pmid:33535456 pmcid:PMC7867112 fatcat:ghiyzev5yfdrtihvczboys2c5i

DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows [article]

Valentin Wolf, Andreas Lugmayr, Martin Danelljan, Luc Van Gool, Radu Timofte
2021 arXiv   pre-print
We validate our DeFlow formulation on the task of joint image restoration and super-resolution.  ...  The difficulty of obtaining paired data remains a major bottleneck for learning image restoration and enhancement models for real-world applications.  ...  Acknowledgements This work was partly supported by the ETH Zürich Fund (OK), a Huawei Technologies Oy (Finland) project, an Amazon AWS grant, a Microsoft Azure grant, and a Nvidia hardware grant.  ... 
arXiv:2101.05796v2 fatcat:orklvmm46zfuzhnfvqklygbhp4

Path-Restore: Learning Network Path Selection for Image Restoration [article]

Ke Yu, Xintao Wang, Chao Dong, Xiaoou Tang, Chen Change Loy
2019 arXiv   pre-print
We conduct experiments on denoising and mixed restoration tasks. The results show that our method could achieve comparable or superior performance to existing approaches with less computational cost.  ...  In particular, our method is effective for real-world denoising, where the noise distribution varies across different regions of a single image.  ...  In Sec. 4.3, we further demonstrate that our method performs well on real-world denoising.  ... 
arXiv:1904.10343v1 fatcat:yrtpoqrndrhd5kfqvfqahhg36e
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