17.June Long Beach, California

NTIRE 2019

New Trends in Image Restoration and Enhancement workshop

and challenges on image and video restoration and enhancement

in conjunction with CVPR 2019


Call for papers

Image restoration and image enhancement are key computer vision tasks, aiming at the restoration of degraded image content, the filling in of missing information, or the needed transformation and/or manipulation to achieve a desired target (with respect to perceptual quality, contents, or performance of apps working on such images). Recent years have witnessed an increased interest from the vision and graphics communities in these fundamental topics of research. Not only has there been a constantly growing flow of related papers, but also substantial progress has been achieved.

Each step forward eases the use of images by people or computers for the fulfillment of further tasks, as image restoration or enhancement serves as an important frontend. Not surprisingly then, there is an ever growing range of applications in fields such as surveillance, the automotive industry, electronics, remote sensing, or medical image analysis etc. The emergence and ubiquitous use of mobile and wearable devices offer another fertile ground for additional applications and faster methods.

This workshop aims to provide an overview of the new trends and advances in those areas. Moreover, it will offer an opportunity for academic and industrial attendees to interact and explore collaborations.

This workshop builds upon the success of the previous NTIRE editions: at CVPR 2017 and 2018 and at ACCV 2016 . Moreover, it relies on all the people associated with the NTIRE past events such as organizers, PC members, distinguished speakers, authors of published paper, challenge participants and winning teams.

Papers addressing topics related to image restoration and enhancement are invited. The topics include, but are not limited to:

  • Image/video inpainting
  • Image/video deblurring
  • Image/video denoising
  • Image/video upsampling and super-resolution
  • Image/video filtering
  • Image/video dehazing
  • Demosaicing
  • Image/video compression
  • Artifact removal
  • Image/video enhancement: brightening, color adjustment, sharpening, etc.
  • Style transfer
  • Image/video generation and hallucination
  • Image/video quality assessment
  • Hyperspectral imaging
  • Underwater imaging
  • Aerial and satellite imaging
  • Methods robust to changing weather conditions / adverse outdoor conditions
  • Perceptual enhancement
  • Studies and applications of the above.

NTIRE 2019 has the following associated groups of challenges:

  • image restoration and enhancement challenges
  • video restoration and enhancement challenges

The authors of the top methods in each category will be invited to submit papers to NTIRE 2019 workshop.

The authors of the top methods will co-author the challenge reports.

The accepted NTIRE workshop papers will be published under the book title "The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops" by

Computer Vision Foundation Open Access and IEEE Xplore Digital Library


Radu Timofte, radu.timofte@vision.ee.ethz.ch

Computer Vision Laboratory

ETH Zurich, Switzerland

Datasets and reports for NTIRE 2019 challenges

Unfortunately, codalab could not recover our NTIRE 2019 challenge pages after a hardware failure. Below are links to the employed datasets and necessary info regarding the challenges.

Important dates

Challenges Event Date (always 5PM Pacific Time)
Site online December 20, 2018
Release of train data and validation data January 10, 2019
Validation server online January 15, 2019
Final test data release, validation server closed March 12, 2019
Test restoration results submission deadline March 24, 2019
Fact sheets submission deadline March 26, 2019
Code/executable submission deadline March 26, 2019
Preliminary test results release to the participants April 3, 2019
Paper submission deadline for entries from the challenges April 14, 2019 23:59 Pacific Time (EXTENDED!)
Workshop Event Date (always 5PM Pacific Time)
Paper submission server online February 1, 2019
Paper submission deadline March 18, 2019 (EXTENDED to 11PM Pacific Time)
Paper submission deadline (only for methods from challenges!) April 14, 2019 23:59 Pacific Time (EXTENDED)
Regular papers decision notification April 6, 2019 (EXTENDED)
Camera ready deadline April 18, 2019
Workshop day June 17, 2019


Instructions and Policies
Format and paper length

A paper submission has to be in English, in pdf format, and at most 8 pages (excluding references) in double column. The paper format must follow the same guidelines as for all CVPR 2019 submissions.

Double-blind review policy

The review process is double blind. Authors do not know the names of the chair/reviewers of their papers. Reviewers do not know the names of the authors.

Dual submission policy

Dual submission is allowed with CVPR2019 main conference only. If a paper is submitted also to CVPR and accepted, the paper cannot be published both at the CVPR and the workshop.

Submission site



Accepted and presented papers will be published after the conference in CVPR Workshops proceedings together with the CVPR2019 main conference papers.

Author Kit

The author kit provides a LaTeX2e template for paper submissions. Please refer to the example egpaper_for_review.pdf for detailed formatting instructions.



Radu Timofte

Radu Timofte is lecturer and research group leader in the Computer Vision Laboratory, at ETH Zurich, Switzerland. He obtained a PhD degree in Electrical Engineering at the KU Leuven, Belgium in 2013, the MSc at the Univ. of Eastern Finland in 2007, and the Dipl. Eng. at the Technical Univ. of Iasi, Romania in 2006. He serves as a reviewer for top journals (such as TPAMI, TIP, IJCV, TNNLS, TCSVT, CVIU, PR) and conferences (ICCV, CVPR, ECCV, NIPS) and is area editor for Elsevier's CVIU journal. He serves as area chair for ACCV 2018 and ICCV 2019. He received a NIPS 2017 best reviewer award. His work received a best scientific paper award at ICPR 2012, the best paper award at CVVT workshop (ECCV 2012), the best paper award at ChaLearn LAP workshop (ICCV 2015), the best scientific poster award at EOS 2017, the honorable mention award at FG 2017, and his team won a number of challenges including traffic sign detection (IJCNN 2013) and apparent age estimation (ICCV 2015). He is co-founder of Merantix and co-organizer of NTIRE, CLIC and PIRM events. His current research interests include sparse and collaborative representations, deep learning, optical flow, image/video compression, restoration and enhancement.

Shuhang Gu

Shuhang Gu received the B.E. degree from the School of Astronautics, Beijing University of Aeronautics and Astronautics, China, in 2010, the M.E. degree from the Institute of Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, China, in 2013, and Ph.D. degree from the Department of Computing, The Hong Kong Polytechnic University, in 2017. He currently holds a post-doctoral position at ETH Zurich, Switzerland. His research interests include image restoration, enhancement and compression.

Ming-Hsuan Yang

Ming-Hsuan Yang received the PhD degree in Computer Science from University of Illinois at Urbana-Champaign. He is a full professor in Electrical Engineering and Computer Science at University of California at Merced. He has published more than 120 papers in the field of computer vision. Yang serves as a program co-chair of ACCV 2014, general co-chair of ACCV 2016, and program co-chair of ICCV 2019. He serves as an editor for PAMI, IJCV, CVIU, IVC and JAIR. His research interests include object detection, tracking, recognition, image deblurring, super resolution, saliency detection, and image/video segmentation.

Lei Zhang

Lei Zhang (M'04, SM'14, F'18) received his B.Sc. degree in 1995 from Shenyang Institute of Aeronautical Engineering, Shenyang, P.R. China, and M.Sc. and Ph.D degrees in Control Theory and Engineering from Northwestern Polytechnical University, Xi'an, P.R. China, respectively in 1998 and 2001, respectively. From 2001 to 2002, he was a research associate in the Department of Computing, The Hong Kong Polytechnic University. From January 2003 to January 2006 he worked as a Postdoctoral Fellow in the Department of Electrical and Computer Engineering, McMaster University, Canada. In 2006, he joined the Department of Computing, The Hong Kong Polytechnic University, as an Assistant Professor. Since July 2017, he has been a Chair Professor in the same department. His research interests include Computer Vision, Pattern Recognition, Image and Video Analysis, and Biometrics, etc. Prof. Zhang has published more than 200 papers in those areas. As of 2018, his publications have been cited more than 36,000 times in the literature. Prof. Zhang is an Associate Editor of IEEE Trans. on Image Processing, SIAM Journal of Imaging Sciences and Image and Vision Computing, etc. He is a "Clarivate Analytics Highly Cited Researcher" from 2015 to 2018.

Luc Van Gool

Luc Van Gool received a degree in electro-mechanical engineering at the Katholieke Universiteit Leuven in 1981. Currently, he is a full professor for Computer Vision at the ETH in Zurich and the Katholieke Universiteit Leuven in Belgium. He leads research and teaches at both places. He has authored over 300 papers. Luc Van Gool has been a program committee member of several, major computer vision conferences (e.g. Program Chair ICCV'05, Beijing, General Chair of ICCV'11, Barcelona, and of ECCV'14, Zurich). His main interests include 3D reconstruction and modeling, object recognition, and tracking and gesture analysis. He received several Best Paper awards (eg. David Marr Prize '98, Best Paper CVPR'07, Tsuji Outstanding Paper Award ACCV'09, Best Vision Paper ICRA'09). In 2015 he received the 5-yearly Excellence Award in Applied Sciences by the Flemish Fund for Scientific Research, in 2016 a Koenderink Prize and in 2017 a PAMI Distinguished Researcher award. He is a co-founder of more than 10 spin-off companies and was the holder of an ERC Advanced Grant (VarCity). Currently, he leads computer vision research for autonomous driving in the context of the Toyota TRACE labs in Leuven and at ETH, as well as image and video enhancement research for Huawei.

Cosmin Ancuti

Cosmin Ancuti received the PhD degree at Hasselt University, Belgium (2009). He was a post-doctoral fellow at IMINDS and Intel Exascience Lab (IMEC), Leuven, Belgium (2010-2012) and a research fellow at University Catholique of Louvain, Belgium (2015-2017). Currently, he is a senior researcher/lecturer at University Politehnica Timisoara. He is the author of more than 50 papers published in international conference proceedings and journals. His area of interests includes image and video enhancement techniques, computational photography and low level computer vision.

Codruta O. Ancuti

Codruta O. Ancuti is a senior researcher/lecturer at University Politehnica Timisoara, Faculty of Electrical and Telecommunication Engineering. She obtained the PhD degree at Hasselt University, Belgium (2011) and between 2015 and 2017 she was a research fellow at University of Girona, Spain (ViCOROB group). Her work received the best paper award at NTIRE 2017 (CVPR workshop). Her main interest of research includes image understanding and visual perception. She is the first that introduced several single images-based enhancing techniques built on the multi-scale fusion (e.g. color-to grayscale, image dehazing, underwater image and video restoration.

Kyoung Mu Lee

Kyoung Mu Lee received the B.S. and M.S. Degrees from Seoul National University, Seoul, Korea, and Ph. D. degree in Electrical Engineering from the University of Southern California in 1993. Currently he is with the Dept. of ECE at Seoul National University as a full professor. His primary research interests include scene understanding, object recognition, low-level vision, visual tracking, and visual navigation. He is currently serving as an AEIC (Associate Editor in Chief) of the IEEE TPAMI, an Area Editor of the Computer Vision and Image Understanding (CVIU), and has served as an Associate Editor of the IEEE TPAMI, the Machine Vision Application (MVA) Journal and the IPSJ Transactions on Computer Vision and Applications (CVA), and the IEEE Signal Processing Letter. He is an Advisory Board Member of CVF (Computer Vision Foundation) and an Editorial Advisory Board Member for Academic Press/Elsevier. He also has served as Area Chars of CVPR, ICCV, ECCV, and ACCV many times, and serves as a general co-chair of ACM MM 2018, ACCV2018 and ICCV2019. He was a Distinguished Lecturer of the Asia-Pacific Signal and Information Processing Association (APSIPA) for 2012-2013.

Michael S. Brown

Michael S. Brown obtained his BS and PhD in Computer Science from the University of Kentucky in 1995 and 2001, respectively. He is currently a professor and Canada Research Chair at York University in Toronto. Dr. Brown has served as an area chair multiple times for CVPR, ICCV, ECCV, and ACCV and was the general chair for CVPR 2018. He has served as an associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and is currently on the editorial board of the International Journal of Computer Vision (IJCV). His research interests including computer vision, image processing, and computer graphics.

Eli Shechtman

Eli Shechtman is a Principal Scientist at the Creative Intelligence Lab at Adobe Research. He received the B.Sc. degree in Electrical Engineering (magna cum laude) from Tel-Aviv University in 1996. Between 2001 and 2007 he attended the Weizmann Institute of Science where he received with honors his M.Sc. and Ph.D. degrees in Applied Mathematics and Computer Science. In 2007 he joined Adobe and started sharing his time as a post-doc with the University of Washington in Seattle. He published over 60 academic publications and holds over 20 issued patents. He served as a Technical Paper Committee member at SIGGRAPH 2013 and 2014, as an Area Chair at CVPR'15, ICCV'15 and CVPR'17 and serves an Associate Editor at TPAMI. He received several honors and awards, including the Best Paper prize at ECCV 2002, a Best Poster Award at CVPR 2004, a Best Reviewer Award at ECCV 2014 and published two Research Highlights papers in the Communication of the ACM journal.

Ming-Yu Liu

Ming-Yu Liu is a principal research scientist at NVIDIA Research. Before joining NVIDIA in 2016, he was a principal research scientist at Mitsubishi Electric Research Labs (MERL). He received his Ph.D. from the Department of Electrical and Computer Engineering at the University of Maryland College Park in 2012. His object pose estimation system was awarded one of hundred most innovative technology products by the R&D magazine in 2014. His street scene understanding paper was selected in the best paper finalist in the 2015 Robotics Science and System (RSS) conference. In CVPR 2018, he won the 1st place in both the Domain Adaptation for Semantic Segmentation Competition in the WAD challenge and the Optical Flow Competition in the Robust Vision Challenge. His research focus is on generative models for image generation and understanding. His goal is to enable machines superhuman-like imagination capabilities.

Zhiwu Huang

Zhiwu Huang is currently a postdoctoral researcher in the Computer Vision Lab, ETH Zurich, Switzerland. He received the PhD degree from Institute of Computing Technology, Chinese Academy of Sciences in 2015. His main research interest is in human-focussed video analysis with Riemannian manifold networks and Wasserstein generative models.

Jianrui Cai

Jianrui Cai received the B.E. and M.E. degrees from the College of Computer Science and Electronic Engineering, Hunan University, China, in 2012 and 2015, respectively. He is currently pursuing the Ph.D. degree with the Department of Computing, The Hong Kong Polytechnic University. His research interests include image processing, computational photography and camera pipeline.

Seungjun Nah

Seungjun Nah is a Ph.D. student at Seoul National University, advised by Prof. Kyoung Mu Lee. He received his BS degree from Seoul National University in 2014. He has worked on research topics including super-resolution, deblurring, and neural network acceleration. He won the 1st place award from NTIRE'17 super-resolution challenge. He volunteered as a reviewer for CVPR 2018, SIGGRAPH Asia 2018. His research interests include enhancing visual quality and deep learning.

Richard Zhang

Richard Zhang is a Research Scientist at Adobe Research, San Francisco. He received his Ph.D. in 2018 in Electrical Engineering and Computer Sciences at the University of California, Berkeley, advised by Professor Alexei A. Efros. Before that, he obtained B.S. and M.Eng. degrees in 2010 from Cornell University in Electrical and Computer Engineering, graduating summa cum laude. He is a recipient of the 2017 Adobe Research Fellowship. His research interests include generative modeling, unsupervised learning, and deep learning.

Andrey Ignatov

Andrey Ignatov is a PhD student at ETH Zurich supervised by Prof. Luc Van Gool and Dr. Radu Timofte. He obtained his master degree from ETH Zurich in 2017. His current research interests include computational imaging, deep learning, wearable devices, and benchmarking.

Abdelrahman Kamel Siddek Abdelhamed

Abdelrahman Abdelhamed is a PhD student at York University supervised by Prof. Michael S. Brown. He obtained his MSc degree in computer science from National University of Singapore in 2016. He received his MSc and BSc degrees in computer science from Assiut University in Egypt in 2014 and 2009, respectively. His MSc thesis received the best MSc thesis award from Assiut University. He has volunteered as a reviewer for WACV'19, BMVC'18, and the IET Computer Vision. He has volunteered as a website chair for CVPR'18. He has received many research awards including the Ontario Trillium Scholarship (York University), AdeptMind Scholarship (AdeptMind), and NUS Research Scholarship (NUS). His current research interests include computer vision, computational imaging, deep learning, and human-computer interaction, with more focus on the problems of image noise modelling, white balance, and uncertainty modelling in neural networks.

Program committee (TBU)

  • Abdelrahman Abdelhamed, York University, Canada
  • Timo Aila, NVIDIA Research
  • Cosmin Ancuti, Universitatea Politehnica Timisoara, Romania
  • Boaz Arad, Ben-Gurion University of the Negev, Israel
  • Nick Barnes, Data61, Australia
  • Yochai Blau, Technion, Israel
  • Michael S. Brown, Samsung Research/York University, Canada
  • Jianrui Cai, Hong Kong Polytechnic University
  • Subhasis Chaudhuri, IIT Bombay, India
  • Sunghyun Cho, DGIST, South Korea
  • Christophe De Vleeschouwer, Universite Catholique de Louvain (UCL), Belgium
  • Chao Dong, SenseTime
  • Weisheng Dong, Xidian University, China
  • Alexey Dosovitskiy, Intel Labs
  • Touradj Ebrahimi, EPFL, Switzerland
  • Michael Elad, Technion, Israel
  • Corneliu Florea, University Politehnica of Bucharest, Romania
  • Alessandro Foi, Tampere University of Technology, Finland
  • Peter Gehler, University of Tuebingen, MPI Intelligent Systems, Amazon, Germany
  • Bastian Goldluecke, University of Konstanz, Germany
  • Luc Van Gool, ETH Zurich and KU Leuven, Belgium
  • Shuhang Gu, ETH Zurich, Switzerland
  • Christine Guillemot, INRIA, France
  • Michael Hirsch, Amazon
  • Hiroto Honda, DeNA Co., Japan
  • Jia-Bin Huang, Virginia Tech, US
  • Zhiwu Huang, ETH Zurich, Switzerland
  • Michal Irani, Weizmann Institute, Israel
  • Phillip Isola, UC Berkeley, US
  • Zhe Hu, Hikvision
  • Sing Bing Kang, Microsoft Research, US
  • Jan Kautz, NVIDIA Research, US
  • Seon Joo Kim, Yonsei University, Korea
  • Vivek Kwatra, Google
  • In So Kweon, KAIST, Korea
  • Christian Ledig, Imagen Technologies, US
  • Kyoung Mu Lee, Seoul National University, South Korea
  • Seungyong Lee, POSTECH, South Korea
  • Stephen Lin, Microsoft Research Asia
  • Ming-Yu Liu, NVIDIA Research
  • Chen Change Loy, Chinese University of Hong Kong
  • Vladimir Lukin, National Aerospace University, Ukraine
  • Kai-Kuang Ma, Nanyang Technological University, Singapore
  • Kede Ma, New York University, US
  • Vasile Manta, Technical University of Iasi, Romania
  • Yasuyuki Matsushita, Osaka University, Japan
  • Roey Mechrez, Technion, Israel
  • Tomer Michaeli, Technion, Israel
  • Peyman Milanfar, Google and UCSC, US
  • Rafael Molina Soriano, University of Granada, Spain
  • Yusuke Monno, Tokyo Institute of Technology, Japan
  • Seungjun Nah, Seoul National University, South Korea
  • Hajime Nagahara, Osaka University, Japan
  • Vinay P. Namboodiri, IIT Kanpur, India
  • Sebastian Nowozin, Google AI (Brain team), Germany
  • Federico Perazzi, Adobe
  • Aleksandra Pizurica, Ghent University, Belgium
  • Sylvain Paris, Adobe
  • Fatih Porikli, Huawei, Australian National University, Australia
  • Hayder Radha, Michigan State University, US
  • Wenqi Ren, Chinese Academy of Sciences
  • Tobias Ritschel, University College London, UK
  • Antonio Robles-Kelly, Deakin University, Australia
  • Stefan Roth, TU Darmstadt, Germany
  • Aline Roumy, INRIA, France
  • Jordi Salvador, Amazon, US
  • Yoichi Sato, University of Tokyo, Japan
  • Gregory Slabaugh, Huawei Noah's Ark Lab
  • Konrad Schindler, ETH Zurich, Switzerland
  • Samuel Schulter, NEC Labs America
  • Nicu Sebe, University of Trento, Italy
  • Eli Shechtman, Adobe Research, US
  • Boxin Shi, Peking University, China
  • Wenzhe Shi, Twitter Inc.
  • Alexander Sorkine-Hornung, Oculus / Facebook
  • Sabine Susstrunk, EPFL, Switzerland
  • Yu-Wing Tai, Tencent
  • Hugues Talbot, Universite Paris Est, France
  • Robby T. Tan, Yale-NUS College, Singapore
  • Masayuki Tanaka, Tokyo Institute of Technology, Japan
  • Jean-Philippe Tarel, IFSTTAR, France
  • Radu Timofte, ETH Zurich, Switzerland
  • George Toderici, Google, US
  • Ashok Veeraraghavan, Rice University, US
  • Jue Wang, Megvii Research, US
  • Oliver Wang, Adobe
  • Chih-Yuan Yang, National Taiwan University
  • Jianchao Yang, Snap Research
  • Ming-Hsuan Yang, University of California at Merced, Google AI
  • Qingxiong Yang, MoonX.AI, China
  • Jong Chul Ye, KAIST, Korea
  • Jason Yosinski, Uber AI Labs, US
  • Wenjun Zeng, Microsoft Research
  • Lei Zhang, The Hong Kong Polytechnic University
  • Richard Zhang, Adobe
  • Wangmeng Zuo, Harbin Institute of Technology, China

Invited Talks

Paolo Favaro

University of Bern

Title:Blind Deconvolution: A Journey from Model-Based to Deep Learning Methods

Abstract: Blind deconvolution has enjoyed quite a remarkable progress in the last few decades thanks to developments in optimization and machine learning. To a large extent today several algorithms allow to recover a sharp image from a blurry one without additional knowledge about the blur. This is a remarkable achievement given the extreme ill-posedness of this mathematical problem. Very interesting steps forward have been made in the last decade, when fundamental inconsistencies in the formulation, such as priors favoring the blurry solution, were exposed. This has led to the study of novel formulations that favor sharp over blurry images and result in state of the art performance with robustness to noise in real images. More recently, developments in deep learning have led to a fundamentally different approach to this problem, where enough data can adequately represent a realistic blur model and allow a neural network to learn how to remove blur from images. Approaches in deep learning have led to surprising results, where rather complex blur artifacts are removed effectively and efficiently. We give an account of the latest developments and show their strengths and weaknesses.

Bio: Paolo Favaro is full professor at the University of Bern, Switzerland, where he heads the Computer Vision Group. He received the Laurea degree (B.Sc.+M.Sc.) from Università di Padova, Italy in 1999, and the M.Sc. and Ph.D. degree in electrical engineering from Washington University in St. Louis in 2002 and 2003 respectively. He was a postdoctoral researcher in the computer science department of the University of California, Los Angeles and subsequently in Cambridge University, UK. Between 2004 and 2006 he worked in medical imaging at Siemens Corporate Research, Princeton, USA. From 2006 to 2011 he was Lecturer and then Reader at Heriot-Watt University and Honorary Fellow at the University of Edinburgh, UK. His research interests are in computer vision, machine learning, computational photography, and image processing.

Tero Karras


Title: A Style-Based Generator Architecture for Generative Adversarial Networks

Abstract: We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.

Bio: Tero Karras is a principal research scientist at NVIDIA Research, which he joined in 2009. His current research interests revolve around deep learning, generative models, and digital content creation. He has also had a pivotal role on NVIDIA's real-time ray tracing efforts, especially related to efficient acceleration structure construction and dedicated hardware units.

Jun-Yan Zhu


Title: Visualizing and Understanding GANs

Abstract: Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications. However, they have not been well visualized or understood. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models. In this talk, we present GAN Dissection, an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to object concepts using a segmentation-based network dissection method. Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. We show several practical applications of our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating artifact-causing units, to interactively manipulating objects in both generated images as well as natural photos.

Bio: Jun-Yan Zhu is a postdoctoral researcher at MIT CSAIL. He obtained his Ph.D. in computer science from UC Berkeley after studying at CMU and UC Berkeley, and before that, received his B.E. from Tsinghua University. He studies computer graphics, computer vision, and machine learning, with the goal of building intelligent machines, capable of recreating the visual world. He is the recipient of Facebook Fellowship, ACM SIGGRAPH Outstanding Doctoral Dissertation Award, and UC Berkeley EECS David J. Sakrison Memorial Prize for outstanding doctoral research.

Sylvain Paris


Title: Photography Made Easy

Abstract: With digital cameras and smartphones, taking a picture has become effortless and easy. Autofocus and autoexposure ensure that all photos are sharp and properly exposed. However, this is not sufficient to get great photos. Most pictures need to be retouched to become aesthetically pleasing. This step still requires a great deal of expertise and a lot of time when done with existing tools. Over the years, I have dedicated a large part of my research to improving this situation. In this talk, I will present a few recent results where we use existing photos by artists as models to make ordinary pictures look better.

Bio: Sylvain Paris is a researcher at Adobe Research in Cambridge Massachusetts. Before that, he was a post-doc at MIT with Frédo Durand and a student at INRIA in Grenoble with François Sillion. His interests cover computational photography and image processing. He has done several contributions to the field of photo and video editing, the goal being to help novices and experts create better pictures and videos. Some of the technology that he has invented is now available in commercial software such as Photoshop and Lightroom.

Chen Change Loy

Nanyang Technological University

Title: Learning Network Path Selection for Image Restoration

Abstract: Very deep Convolutional Neural Networks (CNNs) have greatly improved the performance on various image restoration tasks. However, this comes at a price of increasing computational burden, which limits their practical usages. We believe that some corrupted image regions are inherently easier to restore than others since the distortion and content vary within an image. To this end, we propose Path-Restore, a multi-path CNN with a pathfinder that could dynamically select an appropriate route for each image region. We train the pathfinder using reinforcement learning with a difficulty-regulated reward, which is related to the performance, complexity and the difficulty of restoring a region. 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.

Bio: Chen Change Loy is a Nanyang Associate Professor with the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He is also an Adjunct Associate Professor at the Chinese University of Hong Kong. He received his PhD (2010) in Computer Science from the Queen Mary University of London. Prior to joining NTU, he served as a Research Assistant Professor at the MMLab of the Chinese University of Hong Kong, from 2013 to 2018. He is the recipient of 2019 Nanyang Associate Professorship (Early Career Award) from Nanyang Technological University. His research interests include computer vision and deep learning, with a focus on face analysis, image processing, and visual surveillance. He has published more than 100 papers in top journals and conferences of computer vision and machine learning. He and his team proposed a number of important methods for image super-resolution including SRCNN, SFTGAN and ESRGAN. As a co-author, his journal paper on SRCNN was selected as the `Most Popular Article' by IEEE Transactions on Pattern Analysis and Machine Intelligence in 2016. It remains as one of the top 10 articles to date. ESRGAN has been widely used to remaster various classic games such as Half-Life, Resident Evil 2, Morrowind, and Final Fantasy 7. He serves as an Associate Editor of the International Journal of Computer Vision (IJCV) and IET Computer Vision Journal. He also serves/served as the Area Chair of CVPR 2019, BMVC 2019, ECCV 2018, and BMVC 2018. He is a senior member of IEEE.

Peyman Milanfar


Title: Computation and Photography: How the Mobile Phone Became a Camera

Abstract: The first camera phone was sold in 2000, when taking pictures with your phone was an oddity, and sharing pictures online was unheard-of. Today, barely twenty years later, the smartphone is more camera than phone. How did this happen? This transformation was enabled by advances in computational photography -- the science and engineering of making great images from small form factor, mobile cameras. Modern algorithmic and computing advances have changed the rules of photography, bringing to it new modes of capture, post-processing, storage, and sharing. In this talk, I'll describe some of the history and more recent elements of this technology, including a multi-frame super-resolution and denoising algorithm developed by my team at Google, and launched in Pixel phones.

Bio: Peyman leads the Computational Imaging team in Google Research, part of Google AI Perception. Prior to this, he was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014. He was Associate Dean for Research at the School of Engineering from 2010-12. From 2012-2014 he was on leave at Google-x, where he helped develop the imaging pipeline for Google Glass. Most recently, Peyman's team at Google developed the digital zoom pipeline for the Pixel phones, which includes the multi-frame super-resolution ("Super Res Zoom") pipeline, and the RAISR upscaling algorithm. In addition, the Night Sight mode on Pixel 3 uses our Super Res Zoom technology to merge images (whether you zoom or not) for vivid shots in low light. Peyman received his undergraduate education in electrical engineering and mathematics from the University of California, Berkeley, and the MS and PhD degrees in electrical engineering from the Massachusetts Institute of Technology. He holds 15 patents, several of which are commercially licensed. He founded MotionDSP, which was acquired by Cubic Inc. (NYSE:CUB). Peyman has been keynote speaker at numerous technical conferences including Picture Coding Symposium (PCS), SIAM Imaging Sciences, SPIE, and the International Conference on Multimedia (ICME). Along with his students, he has won several best paper awards from the IEEE Signal Processing Society. He is a Distinguished Lecturer of the IEEE Signal Processing Society, and a Fellow of the IEEE "for contributions to inverse problems and super-resolution in imaging."

Chiu Man Ho


Title: Imaging in the Dark

Abstract: Imaging under dark scenes has become a hot topic in the smartphone industry. In this talk, I will give an overview of the topic. After that, I will discuss OPPO’s effort along this direction and show some demos.

Bio: Chiu Man Ho is the Director of AI at OPPO US R&D. He got a PhD in theoretical physics from University of Pittsburgh and did a postdoc at UC Berkeley. Before leaving the academia, he was a Research Assistant Professor at Michigan State University. Prior to joining OPPO US R&D, he was a Senior Staff Data Scientist at Huawei US R&D. Chiu Man is highly interested in AI research and its commercialization. He also helps to develop the AI strategies for OPPO and leads a team to apply AI to improve user experience. He works on deep learning, generative adversarial network, and reinforcement learning. He is broadly interested in applying these techniques to computer vision and natural language processing. Currently, he focuses on video understanding. He also works to accelerate and compress deep learning models.


The accepted NTIRE workshop papers will be published under the book title "2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops" by

Computer Vision Foundation Open Access and IEEE Xplore Digital Library

Poster setting (all papers have poster panels for the whole day, CVPR guidelines)

(Poster #76) Real Photographs Denoising with Noise Domain Adaptation and Attentive Generative Adversarial Network
Kai Lin, Thomas H Li, Shan Liu, Ge Li
(Poster #77) Multi-level Encoder-Decoder Architectures for Image Restoration
Indra Deep Mastan, Shanmuganathan Raman
(Poster #78) Learning Deep Image Priors for Blind Image Denoising
Xianxu Hou, Hongming Luo, Jingxin Liu, Bolei Xu, Ke Sun, Yuanhao Gong, Bozhi Liu, Guoping Qiu
(Poster #79) Conditional GANs for Multi-Illuminant Color Constancy: Revolution or Yet Another Approach?
Oleksii Sidorov
(Poster #80) Deep Graph Laplacian Regularization for Robust Denoising of Real Images
Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene Cheung
(Poster #81) Multi-stage Optimization for Photorealistic Neural Style Transfer
Richard Yang
(Poster #82) Natural Image Noise Dataset
Benoit Brummer, Christophe De Vleeschouwer
(Poster #83) VORNet: Spatio-temporally Consistent Video Inpainting for Object Removal
Ya-Liang Chang, Zhe Yu Liu, Winston Hsu
(Poster #84) DenseNet with Deep Residual Channel-Attention Blocks for Single Image Super Resolution
Dong-Won Jang, Rae-Hong Park
(Poster #85) Light Field Super-Resolution: A Benchmark
Zhen Cheng, Zhiwei Xiong, Chang Chen, Dong Liu
(Poster #86) Exemplar Guided Face Image Super-Resolution without Facial Landmarks
Berk Dogan, Shuhang Gu, Radu Timofte
(Poster #87) Recursive Image Dehazing via Perceptually Optimized Generative Adversarial Network (POGAN)
Yixin Du, Xin Li
(Poster #88) Aspect-Ratio-Preserving Multi-Patch Image Aesthetics Score Prediction
Lijie Wang, Xueting Wang, Toshihiko Yamasaki, Kiyoharu Aizawa
(Poster #89) ViDeNN: Deep Blind Video Denoising
Michele Claus, Jan van Gemert
(Poster #90) An Epipolar Volume Autoencoder with Adversarial Loss for Deep Light Field Super-Resolution
Minchen Zhu, Anna Alperovich, Ole Johannsen, Antonin Sulc, Bastian Goldluecke
(Poster #91) Evaluating Parameterization Methods for Convolutional Neural Network (CNN)-Based Image Operators
Seung-Wook Kim, Sungjin Cho, Kwang Hyun Uhm, Seo-Won Ji, Sang-Won Lee, Sung-Jea Ko
(Poster #92) Edge Detection Techniques for Quantifying Spatial Imaging System Performance and Image Quality
Oliver van Zwanenberg, Sophie Triantaphillidou, Robin Jenkin, Aleka Psarrou
(Poster #93) Histogram Learning in Image Contrast Enhancement
Bin Xiao, Yunqiu Xu, Han Tang, Xiuli Bi, Weisheng Li
(Poster #94) Optimization-Based Data Generation for Photo Enhancement
Mayu Omiya, Yusuke Horiuchi, Edgar Simo-Serra, Satoshi Iizuka, Hiroshi Ishikawa
(Poster #95) FRESCO: Fast Radiometric Egocentric Screen Compensation
Matthew Post, Paul Fieguth, Mohamed Naiel, Zohreh Azimifar, Mark Lamm
(Poster #96) Low Rank Poisson Denoising (LRPD): A low rank approach using split Bregman algorithm for Poisson noise removal from images
Prashanth G, Rajiv Sahay
(Poster #97) Kalman filtering of patches for frame-recursive video denoising
Pablo Arias, Jean-Michel Morel
(Poster #98) High-Resolution Single Image Dehazing using Encoder-Decoder Architecture
Simone Bianco, Luigi Celona, Flavio Piccoli, Raimondo Schettini
(Poster #99) Content-preserving Tone Adjustment for Image Enhancement
Simone Bianco, Claudio Cusano, Flavio Piccoli, Raimondo Schettini
(Poster #100) Orientation-aware Deep Neural Network for Real Image Super-Resolution
Du Chen, Zewei He, Anshun Sun, Jiangxin Yang, Yanlong Cao, Yanpeng Cao, Siliang Tang, Michael Ying Yang
(Poster #101) EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
Xintao Wang, Kelvin C.K. Chan, Ke Yu, Chao Dong, Chen Change Loy
(Poster #102) Suppressing Model Overfitting for Image Super-Resolution Networks
Ruicheng Feng, Jinjin Gu, Yu Qiao, Chao Dong
(Poster #103) NTIRE 2019 Challenge on Video Deblurring: Methods and Results
Seungjun Nah, Radu Timofte, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Kyoung Mu Lee et al.
(Poster #104) NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results
Seungjun Nah, Radu Timofte, Shuhang Gu, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Kyoung Mu Lee et al.
(Poster #105) 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
(Poster #106) Multi-scale deep neural networks for real image super-resolution
Shangqi Gao, Xiahai Zhuang
(Poster #107) RI-GAN: An End-to-End Network for Single Image Haze Removal
Akshay Dudhane, Harsh Aulakh, Subrahmanyam Murala
(Poster #108) SCAN: Spatial Color Attention Networks for Real Single Image Super-Resolution
Xuan Xu, Xin Li
(Poster #109) Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution
Chao Li, Dongliang He, Xiao Liu, Yukang Ding, Shilei Wen
(Poster #110) Hierarchical Back Projection Network for Image Super-Resolution
Zhisong Liu, Li-Wen Wang, Chu-Tak Li, Wan-Chi Siu
(Poster #111) Multi-scale Adaptive Dehazing Network
Shuxin Chen, Yizi Chen, Yanyun Qu, Jingying Huang, Ming Hong
(Poster #112) MultiBoot VSR: Multi-Stage Multi-Reference Bootstrapping for Video Super-Resolution
Ratheesh Kalarot, Fatih Porikli
(Poster #113) Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
Jiaming Liu, Chi-Hao Wu, Yuzhi Wang, Qin Xu, Yuqian Zhou, Haibin Huang, Chuan Wang, Shaofan Cai, Yifan Ding, Haoqiang Fan, Jue Wang
(Poster #114) Feature Forwarding for Efficient Single Image Dehazing
Peter Morales, Tzofi Klinghoffer
(Poster #115) GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling
Dong-Wook Kim, Jae Ryun Chung, Seung-Won Jung
(Poster #116) Deep Iterative Down-Up CNN for Image Denoising
Songhyun Yu, Bumjun Park, Jechang Jeong
(Poster #117) Densely Connected Hierarchical Network for Image Denoising
Bumjun Park, Songhyun Yu, Jechang Jeong
(Poster #118) Fractal Residual Network and Solutions for Real Super-Resolution
Junhyung Kwak, Donghee Son
(Poster #119) Dense Scene Information Estimation Network for Dehazing
Tiantong Guo, Xuelu Li, Venkateswararao Cherukuri, Vishal Monga
(Poster #120) Dense `123' Color Enhancement Dehazing Network
Tiantong Guo, Venkateswararao Cherukuri, Vishal Monga
(Poster #121) A Deep Motion Deblurring Network based on Per-Pixel Adaptive Kernels with Residual Down-Up and Up-Down Modules
Hyeonjun Sim, Munchurl Kim
(Poster #122) Image Colorization By Capsule Networks
Gokhan Ozbulak
(Poster #123) An Empirical Investigation of Efficient Spatio-Temporal Modeling in Video Restoration
Yuchen Fan, Jiahui Yu, Ding Liu, Thomas Huang
(Poster #124) Encoder-Decoder Residual Network for Real Super-resolution
Guoan Cheng, Ai Matsune, Qiuyu Li, Leilei Zhu, Huaijuan Zang, Shu Zhan
(Poster #125) GANmera: Reproducing Aesthetically Pleasing Photographs using Deep Adversarial Networks
Nelson Chong, Lai-Kuan Wong, John See
(Poster #127) NTIRE 2019 Challenge on Real Image Denoising: Methods and Results
Abdelrahman Abdelhamed, Radu Timofte, Michael S Brown, et al.
(Poster #128) NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results
Jianrui Cai, Shuhang Gu, Radu Timofte, Lei Zhang, et al.
(Poster #130pm or #74am) NTIRE 2019 Challenge on Image Enhancement: Methods and Results
Andrey Ignatov, Radu Timofte, et al.
(Poster #130pm or #74am) NTIRE 2019 Challenge on Image Colorization: Report
Shuhang Gu, Radu Timofte, Richard Zhang, et al.
(Poster #131pm or #75am) NTIRE 2019 Image Dehazing Challenge Report
Codruta O Ancuti, Cosmin Ancuti, Radu Timofte, et al.
(CVPR oral, Poster #129) Underexposed Photo Enhancement Using Deep Illumination Estimation
Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, Jiaya Jia