23.October Tel-Aviv, Israel

AIM 2022

Advances in Image Manipulation workshop

in conjunction with ECCV 2022

Join AIM 2022 workshop online Zoom for LIVE, talks, Q&A, interaction

The event starts 23.10.2022 at 2:00 EDT / 06:00 UTC / 09:00 Israel time / 14:00 China time.
Check the AIM 2022 schedule.
The recording of the whole AIM 2022 event:


Call for papers

Image manipulation is a 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 manipulation 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 Advances in Image Manipulation (AIM) workshop at ICCV 2021, ECCV 2020,ICCV 2019, Mobile AI (MAI) workshop at CVPR 2022 , CVPR 2021 , Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018 , the workshop and Challenge on Learned Image Compression (CLIC) editions at CVPR 2018, CVPR 2019, CVPR 2020, CVPR 2021, , CVPR 2022 and the New Trends in Image Restoration and Enhancement (NTIRE) editions: at CVPR 2017 , 2018, 2019 , 2020 , 2021 and 2022 and at ACCV 2016. Moreover, it relies on the people associated with the PIRM, CLIC, MAI, AIM, and NTIRE events such as organizers, PC members, distinguished speakers, authors of published papers, challenge participants and winning teams.

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

  • Image-to-image translation
  • Video-to-video translation
  • Image/video manipulation
  • Perceptual manipulation
  • Image/video generation and hallucination
  • Image/video quality assessment
  • Image/video semantic segmentation
  • Perceptual enhancement
  • Multimodal translation
  • Depth estimation
  • Saliency and gaze estimation
  • Image/video inpainting
  • Image/video deblurring
  • Image/video denoising
  • Image/video upsampling and super-resolution
  • Image/video filtering
  • Image/video de-hazing, de-raining, de-snowing, etc.
  • Demosaicing
  • Image/video compression
  • Removal of artifacts, shadows, glare and reflections, etc.
  • Image/video enhancement: brightening, color adjustment, sharpening, etc.
  • Style transfer
  • Hyperspectral imaging
  • Underwater imaging
  • Aerial and satellite imaging
  • Methods robust to changing weather conditions / adverse outdoor conditions
  • Image/video manipulation on mobile devices
  • Image/video restoration and enhancement on mobile devices
  • Studies and applications of the above.

Important dates

Challenges Event Date (always 23:59 CET)
Site online May 23, 2022
Release of train data and validation data May 24, 2022
Validation server online June 1, 2022
Final test data release, validation server closed July 23, 2022
Test phase submission deadline July 30, 2022
Fact sheets, code/executable submission deadline July 30, 2022
Preliminary test results release to the participants August 2, 2022
Paper submission deadline for entries from the challenges August 14, 2022 (EXTENDED)
Workshop Event Date (always 23:59 CET)
Paper submission deadline July 31, 2022 (EXTENDED)
Paper submission deadline (only for methods from AIM 2022 and Mobile AI 2022 challenges and papers reviewed elsewhere!) August 14, 2022 (EXTENDED)
Paper decision notification August 15, 2022
Camera ready deadline August 22, 2022
Workshop day October 23, 2022 (VIRTUAL)


Instructions and Policies
Format and paper length

A paper submission has to be in English, in pdf format, and at most 14 pages (excluding references) in single-column, ECCV style. The paper format must follow the same guidelines as for all ECCV 2022 submissions.
AIM 2022 and ECCV 2022 author guidelines

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 not allowed. If a paper is submitted also to ECCV and accepted, the paper cannot be published both at the ECCV and the workshop.

Submission site



Accepted and presented papers will be published after the conference in ECCV Workshops proceedings together with the ECCV2022 main conference papers.

Author Kit

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


Organizers (TBU)

  • Radu Timofte, University of Wurzburg and ETH Zurich,
  • Andrey Ignatov, AI Benchmark and ETH Zurich,
  • Ren Yang, ETH Zurich,
  • Marcos V. Conde, University of Wurzburg,
  • Furkan Kınlı, Özyeğin University,

PC Members (TBU)

  • Codruta Ancuti, UPT
  • Boaz Arad, Ben-Gurion University of the Negev
  • Siavash Arjomand Bigdeli, DTU
  • Michael S. Brown, York University
  • Jianrui Cai, The Hong Kong Polytechnic University
  • Chia-Ming Cheng, MediaTek
  • Cheng-Ming Chiang, MediaTek
  • Sunghyun Cho, Samsung
  • Marcos V. Conde, University of Wurzburg
  • Chao Dong, SIAT
  • Weisheng Dong, Xidian University
  • Touradj Ebrahimi, EPFL
  • Paolo Favaro, University of Bern
  • Graham Finlayson, University of East Anglia
  • Corneliu Florea, University Politechnica of Bucharest
  • Bastian Goldluecke, University of Konstanz
  • Shuhang Gu, OPPO & University of Sydney
  • Christine Guillemot, INRIA
  • Felix Heide, Princeton University & Algolux
  • Chiu Man Ho, OPPO,
  • Hiroto Honda, Mobility Technologies Co Ltd.
  • Andrey Ignatov, ETH Zurich
  • Aggelos Katsaggelos, Northwestern University
  • Jan Kautz, NVIDIA
  • Furkan Kınlı, Özyeğin University
  • Christian Ledig, University of Bamberg
  • Seungyong Lee, POSTECH
  • Kyoung Mu Lee, Seoul National University
  • Juncheng Li, The Chinese University of Hong Kong
  • Yawei Li, ETH Zurich
  • Stephen Lin, Microsoft Research
  • Guo Lu, Beijing Institute of Technology
  • Kede Ma, City University of Hong Kong
  • Vasile Manta, Technical University of Iasi
  • Rafal Mantiuk, University of Cambridge
  • Zibo Meng, OPPO
  • Yusuke Monno, Tokyo Institute of Technology
  • Hajime Nagahara, Osaka University
  • Vinay P. Namboodiri, University of Bath/li>
  • Federico Perazzi, Bending Spoons
  • Fatih Porikli, Qualcomm CR&D
  • Antonio Robles-Kelly, Deakin University
  • Aline Roumy, INRIA
  • Christopher Schroers, Disney Research | Studios
  • Nicu Sebe, University of Trento
  • Eli Shechtman, Creative Intelligence Lab at Adobe Research
  • Gregory Slabaugh, Queen Mary University of London
  • Sabine Süsstrunk, EPFL
  • Yu-Wing Tai, Kuaishou Technology & HKUST
  • Robby T. Tan, Yale-NUS College
  • Masayuki Tanaka, Tokyo Institute of Technology
  • Hao Tang, ETH Zurich
  • Qi Tian, Huawei Cloud & AI
  • Radu Timofte, University of Wurzburg & ETH Zurich
  • George Toderici, Google
  • Luc Van Gool, ETH Zurich & KU Leuven
  • Longguang Wang, National University of Defense Technology
  • Yingqian Wang, National University of Defense Technology
  • Gordon Wetzstein, Stanford University
  • Ming-Hsuan Yang, University of California at Merced & Google
  • Ren Yang, ETH Zurich
  • Wenjun Zeng, Microsoft Research
  • Kai Zhang, ETH Zurich
  • Yulun Zhang, ETH Zurich
  • Jun-Yan Zhu, Carnegie Mellon University
  • Wangmeng Zuo, Harbin Institute of Technology

Invited Talks

Sabine Süsstrunk


Title: Uncovering local semantics in CNNs and GANs

Abstract: Automatically localizing similar semantic concepts within an image or a set of images allows for many applications, such as image segmentation, localization, and image editing. We propose Deep Feature Factorization (DFF), a method capable of detecting hierarchical cluster structures in feature space of a convolutional neural network (CNN). These clusters are visualized as heat maps, which highlight semantically matching regions across a set of images, revealing what the network `perceives' as similar. Analogue structures can also be found in generative adversarial networks (GANs). Focusing on StyleGAN, we introduce two simple and effective methods for making local, semantically-aware edits to a GAN output image. Our methods require neither supervision from an external model, nor involve complex spatial morphing operations. Semantic editing is demonstrated on variety of scenes, and even real photographs using GAN inversion.

Bio: Sabine Süsstrunk is Full Professor and Director of the Image and Visual Representation Lab in the School of Computer and Communication Sciences (IC) at the Ecole Polytechnique Fédérale (EPFL), Lausanne, Switzerland. Her main research areas are in computational imaging, computer vision, machine learning, and computational image quality and aesthetics. She is President of the Swiss Science Council SSC, Founding Member and Member of the Board of the EPFL-WISH (Women in Science and Humanities) Foundation, board member of the SRG SSR (Swiss Radio and Television Corporation), and Co-Founder and board member of Largo Films, Ltd. Sabine is a Fellow of IEEE, IS&T, and the Swiss Academy of Engineering Sciences (SATW).

Felix Heide

Princeton University & Algolux

Title: The Differentiable Camera: Designing Cameras to Detect the Invisible

Abstract: Although today's cameras fuel diverse applications, from personal photography to self-driving vehicles, they are designed in a compartmentalized fashion where the optics, sensor, image processing pipeline, and vision models are often devised in isolation: a camera design is decided by intermediate metrics describing optical performance, signal to noise ratio, and image quality, even though object detection scores may only matter for the camera application. In this talk, I will present a differentiable camera architecture, including compound optics, sensing and exposure control, image processing, and downstream vision models. This architecture allows us to learn cameras akin to neural networks, entirely guided by downstream loss functions. Learned cameras move computation to the optics, with entirely different optical stacks for different vision tasks (and beating existing stacks such as Tesla's Autopilot). The approach allows us to learn entirely new domain-specific cameras that perform imaging and vision tasks jointly, and learn active illumination together with the image pipeline, achieving accurate dense depth and vision tasks in heavy fog, snow, and rain (beating scanning lidar methods). Finally, I will describe an approach that makes the scene itself differentiable, allowing us to backpropagate gradients through the entire capture and processing chain in an inverse rendering fashion. As such, the proposed novel breed of learned cameras brings unprecedented capabilities in optical design, imaging, and vision.

Bio: Felix Heide is an Assistant Professor at Princeton University and Co-Founder and Chief Technology Officer of self-driving vehicle startup Algolux. He is researching the theory and application of computational imaging. As such, Felix's work lies at the intersection of optics, machine learning, optimization, computer graphics, and computer vision. Felix received his Ph.D. from the University of British Columbia. He obtained his MSc from the University of Siegen and was a postdoc at Stanford University. His doctoral dissertation won the Alain Fournier Ph.D. Dissertation Award and the SIGGRAPH outstanding doctoral dissertation award. He won an NSF CAREER Award and Sony Young Faculty Award 2021. He co-founded the autonomous driving startup Algolux, and his research is directly used by Waymo, Cruise, and Google as part of their autonomous vehicle programs.

Gordon Wetzstein

Stanford University

Title: Efficient Neural Scene Representation, Rendering, and Generation

Abstract: Neural radiance fields and scene representation networks offer unprecedented capabilities for photorealistic scene representation, view interpolation, and many other tasks. In this talk, we discuss expressive scene representation network architecture, efficient neural rendering approaches, and generalization strategies that allow us to generate photorealistic multi-view-consistent humans or cats using state-of-the-art 3D GANs.

Bio: Gordon Wetzstein is an Associate Professor of Electrical Engineering and, by courtesy, of Computer Science at Stanford University. He is the leader of the Stanford Computational Imaging Lab and a faculty co-director of the Stanford Center for Image Systems Engineering. At the intersection of computer graphics and vision, artificial intelligence, computational optics, and applied vision science, Prof. Wetzstein's research has a wide range of applications in next-generation imaging, wearable computing, and neural rendering systems. Prof. Wetzstein is the recipient of numerous awards, including an NSF CAREER Award, an Alfred P. Sloan Fellowship, an ACM SIGGRAPH Significant New Researcher Award, a Presidential Early Career Award for Scientists and Engineers (PECASE), an SPIE Early Career Achievement Award, an Electronic Imaging Scientist of the Year Award, an Alain Fournier Ph.D. Dissertation Award as well as many Best Paper and Demo Awards.

Bjorn Ommer

University of Munich

Title: Stable Diffusion++: Democratizing Visual Synthesis

Abstract: The ultimate goal of computer vision are models that can understand our (visual) world. Recent deep generative models for visual synthesis open up new avenues towards scene understanding by providing accurate representations of both, the rich details and the diversity featured by large, heterogeneous image datasets. Still, they exhibit specific limitations that restrict their applicability and performance especially in complex tasks such as high-resolution image synthesis. We will discuss a solution, latent diffusion models a.k.a. "Stable Diffusion", that significantly improves the efficiency of diffusion models. Now billions of training samples can be summarized in compact representations of just a few gigabyte so that the approach runs on even consumer GPUs, thus making high-quality visual synthesis accessible for everyone. We will then discuss ongoing work that shows what we should better NOT learn to represent with generative models and we instead propose a semi-parametric approach.

Bio: Björn Ommer is a full professor at the University of Munich where he is heading the Machine Vision and Learning Group. Before he was a full professor in the department of mathematics and computer science at Heidelberg University. He received his diploma in computer science from University of Bonn and his PhD from ETH Zurich. Thereafter, he was a postdoc in the vision group of Jitendra Malik at UC Berkeley. Björn serves as an associate editor for IEEE T-PAMI. His research interests include semantic scene understanding, visual synthesis and retrieval, self-supervised metric and representation learning, and explainable AI. Moreover, he is applying this basic research in interdisciplinary projects within the digital humanities and the life sciences.

Bo Zhu

Meta Reality Labs

Title: Practical considerations for on-device denoising

Abstract: This talk addresses some practical considerations of ML-based denoising for on-device contexts. We will discuss rapid and inexpensive methods for photon transfer curve characterization without the need for flat field illuminators and integration spheres, the importance of variance stabilization transforms in quantized, computationally efficient models, as well as compression of raw bayer data for offloaded inference scenarios.

Bio: Bo Zhu is a Research Scientist Manager of AI at Meta Reality Labs, leading a computer vision research team to advance imaging performance in AR & VR devices. Prior to joining Meta, Bo was cofounder and CTO of BlinkAI, a spinoff from ML image reconstruction research (Nature, 2018) he led as a postdoc at Harvard, whose AI video denoising products were commercially deployed on tens of millions of devices globally (including DXOMark’s #1 ranked camera phone in 2021, Mi 11 Ultra) and awarded 2021 Product of the Year at Embedded Vision Summit. He obtained his S.B., M.Eng., and Ph.D. from MIT and performed research at the Martinos Center for Biomedical Imaging and CSAIL.

Pratul Srinivasan

Google Research

Title: Neural Inverse Rendering

Abstract: Recent progress in neural 3D object and scene representations such as Neural Radiance Fields (NeRFs) are bringing us closer to tackling the longstanding inverse rendering problem of jointly estimating geometry, materials, and lighting from observed images. In this talk, I will discuss how current neural field approaches to inverse rendering can be thought of as lying on a spectrum of simulation vs. precomputation, and how this suggests potentially interesting and fruitful research directions.

Bio: Pratul Srinivasan is a research scientist at Google Research, where he works on problems at the intersection of computer vision and graphics. His recent work has focused on view synthesis, inverse rendering, and 3D reconstruction. He completed his PhD at UC Berkeley in 2020, where he was advised by Ravi Ramamoorthi and Ren Ng and supported by an NSF Graduate Fellowship. His research has been recognized by the David J. Sakrison Memorial Prize from UC Berkeley in 2020, the ACM Doctoral Dissertation Award Honorable Mention in 2022, the ECCV Best Paper Honorable Mention Award in 2020, the ICCV Best Paper Honorable Mention Award in 2021, and the CVPR Best Student Paper Honorable Mention Award in 2022.

All the accepted AIM workshop papers have oral presentation.
All the accepted AIM workshop papers are published under the book title "European Conference on Computer Vision Workshops (ECCVW)" by


papers (pdf, suppl. mat) available at https://eccv2022.ecva.net/

[06:10 UTC] 09:10 Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report (# 369 )
Andrey Ignatov (ETH Zurich)*; Radu Timofte (University of Wurzburg & ETH Zurich) et al.
[06:20 UTC] 09:20 Adaptive Mask-Based Pyramid Network for Realistic Bokeh Rendering (# 343 )
Kostas Georgiadis (CERTH/ITI); Albert Saà-Garriga (Samsung R&D UK); Mehmet Kerim Yücel (Samsung R&D UK )*; Anastasios Drosou (CERTH-ITI); Bruno Manganelli (Samsung Research UK)
[06:25 UTC] 09:25 Bokeh-Loss GAN: Multi-stage Adversarial Training for Realistic Edge-Aware Bokeh (# 356 )
Brian J Lee (SenseBrain Technology Limited)*; Fei Lei (Tetras.AI); Huaijin Chen (SenseBrain Technology Limited); Alexis Baudron (SenseBrain Technology Limited )
[06:30 UTC] 09:30 Style Adaptive Semantic Image Editing with Transformers (# 329 )
Edward A Günther (ETH Zürich); Rui Gong (ETH Zurich)*; Luc Van Gool (ETH Zurich)
[06:35 UTC] 09:35 Third Time’s the Charm? Image and Video Editing with StyleGAN3 (# 330 )
Yuval Alaluf (Tel Aviv University)*; Or Patashnik (Tel Aviv University); Zongze Wu (Hebrew University of Jerusalem); Asif Zamir (Tel Aviv university ); Eli Shechtman (Adobe Research, US); Dani Lischinski (The Hebrew University of Jerusalem); Danny Cohen-Or (Tel Aviv University)
[06:40 UTC] 09:40 CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal (# 331 )
Qianhao Yu (University of Science and Technology of China); Naishan Zheng (University of Science and Technology of China); Jie Huang (University of Science and Technology of China); Feng Zhao (University of Science and Technology of China)*
[06:45 UTC] 09:45 Unifying Conditional and Unconditional Semantic Image Synthesis with OCO-GAN (# 332 )
Marlène Careil (Facebook/Télécom Paris)*; Stéphane Lathuilière (Telecom-Paris); Camille Couprie (FAIR); Jakob Verbeek (Facebook)
[07:30 UTC] 10:30 Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report (# 366 )
Andrey Ignatov (ETH Zurich)*; Grigory Malivenko (AI); Radu Timofte (University of Wurzburg & ETH Zurich) et al.
[07:40 UTC] 10:40 Hybrid Transformer Based Feature Fusion for Self-Supervised Monocular Depth Estimation (# 336 )
Snehal Singh Tomar (Indian Institute of Technology Madras)*; Maitreya Suin (Indian Institute of Technology Madras); Rajagopalan N Ambasamudram (Indian Institute of Technology Madras)
[07:45 UTC] 10:45 LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices (# 348 )
Zhenyu Li (Harbin Institute of Technology)*; Zehui Chen (University of Science and Technology of China); Jialei Xu (Harbin Institute of Technology); Xianming Liu (Harbin Institute of Technology); Junjun Jiang (Harbin Institute of Technology)
[07:50 UTC] 10:50 U-Shape Transformer for Underwater Image Enhancement (# 335 )
Lintao Peng (Beijing Institute of Technology); Chunli Zhu (Beijing Institute of Technology); Liheng Bian (Beijing Institute of Technology)*
[07:55 UTC] 10:55 Multi-Patch Learning: Looking More Pixels in the Training Phase (# 351 )
Lei Li (ByteDance Inc)*; Jingzhu Tang (Xidian University); Ming Cheng (ByteDance Inc); Shijie Zhao (Bytedance Inc.); Junlin Li (ByteDance Inc.); Li Zhang (Bytedance Inc.)
[08:30 UTC] 11:30 Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report (# 365 )
Andrey Ignatov (ETH Zurich)*; Radu Timofte (University of Wurzburg & ETH Zurich) et al.
[08:40 UTC] 11:40 Residual Feature Distillation Channel Spatial Attention Network for ISP on Smartphone (# 357 )
yaqi wu (Harbin Institute of Technology)*; Jas zheng (zhejiang university); zhihao Fan (University of Shanghai for Science and Technology); Xun Wu (school of software, tsinghua university); Feng Zhang (AIS)
[08:45 UTC] 11:45 Real-Time Under-Display Cameras Image Restoration and HDR on Mobile Devices (# 372 )
Marcos V. Conde (University of Würzburg)*; Florin-Alexandru Vasluianu (Computer Vision Lab, University of Wurzburg); Sabari Nathan (Couger Inc, Tokyo); Radu Timofte (University of Wurzburg & ETH Zurich)
[08:50 UTC] 11:50 CEN-HDR: Computationally Efficient Neural Network for Real-Time High Dynamic Range Imaging (# 340 )
Steven Tel (University of Burgundy, France)*; Barth Heyrman (University of Burgundy, France); Dominique Ginhac (Le2i - University of Burgundy, France)
[08:55 UTC] 11:55 MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning (# 370 )
Andrey Ignatov (ETH Zurich)*; Anastasia Sycheva (Altersis Performance); Radu Timofte (University of Wurzburg & ETH Zurich); Yu Tseng (mediatek); Yu-Syuan Xu (MediaTek); Po-Hsiang Yu (Mediatek); Cheng-Ming Chiang (MediaTek Inc.); Hsien-Kai Kuo (MediaTek); Min-Hung Chen (Microsoft); Chia-Ming Cheng (MediaTek); Luc Van Gool (ETH Zurich)
[09:30 UTC] 12:30 Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report (# 363 )
Marcos V. Conde (University of Würzburg)*; Radu Timofte (University of Wurzburg & ETH Zurich); et al.
[09:40 UTC] 12:40 Learned Reverse ISP with Soft Supervision (# 347 )
Beiji Zou (Central South University); Yue Zhang (Central South University)*
[09:50 UTC] 12:50 RISPNet: A Network for Reversed Image Signal Processing (# 344 )
Xiaoyi Dong (Institute of Automation, Chinese Academy of Sciences)*; Yu Zhu (School of Computer Science and Engineering,Anhui University); Chenghua Li (Institute of Automation Chinese Academy of Sciences); Peisong Wang (Institute of Automation, Chinese Academy of Sciences); Jian Cheng ("Chinese Academy of Sciences, China")
[09:55 UTC] 12:55 Reversing Image Signal Processors by Reverse Style Transferring (# 360 )
Furkan Osman Kınlı (Özyeğin University)*; Barış Özcan (Özyeğin University); Furkan Kirac (Ozyegin University)
[10:00 UTC] 13:00 Overexposure Mask Fusion: Generalizable Reverse ISP Multi-step Refinement (# 361 )
Jinha Kim (MIT); Jun Jiang (SenseBrain Research (USA))*; Jinwei Gu (SenseBrain)
[11:30 UTC] 14:30 Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI \& AIM 2022 challenge: Report (# 367 )
Andrey Ignatov (ETH Zurich)*; Radu Timofte (University of Wurzburg & ETH Zurich); Maurizio Denna (Synaptics); Abdel Younes (Synaptics) et al.
[11:40 UTC] 14:40 Efficient Image Super-Resolution Using Vast-Receptive-Field Attention (# 333 )
Lin Zhou (Shenzhen Institues Of Advanced Technology, Chinese Academy of sciences); Haoming CAI (University of Maryland, College Park)*; Jinjin Gu (The University of Sydney); Zheyuan Li (SIAT); Yingqi Liu (Shenzhen Institute of Advanced Technology); Xiangyu Chen (University of Macau; SIAT); Yu Qiao (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences); Chao Dong (SIAT)
[11:45 UTC] 14:45 Fast Nearest Convolution for Real-Time Efficient Image Super-Resolution (# 352 )
Ziwei Luo (Uppsala)*; Youwei Li (Megvii); lei yu (Megvii); Qi Wu (Megvii); wen zhihong (MEGVII technology); Haoqiang Fan (Megvii Inc(face++)); Shuaicheng Liu (UESTC; Megvii)
[11:50 UTC] 14:50 Real-Time Channel Mixing Net for Mobile Image Super-Resolution (# 353 )
Garas Gendy (Shanghai Jiao Tong University ); nabil sabor (Assiut University); Jingchao Hou (Shanghai Jiao Tong University); Guanghui He (Shanghai Jiao tong University)*
[11:55 UTC] 14:55 DSR: Towards Drone Image Super-Resolution (# 339 )
Xiaoyu Lin (EPFL)*; Baran Ozaydin (EPFL); Vidit Vidit (EPFL); Majed El Helou (EPFL); Sabine Süsstrunk (EPFL)
[12:00 UTC] 15:00 Evaluating Image Super-resolution Performance on Mobile Devices: An Online Benchmark (# 328 )
Xindong Zhang (The Hong Kong Polytechnic University); Hui Zeng (OPPO); Lei Zhang ("Hong Kong Polytechnic University, Hong Kong, China")*
[12:05 UTC] 15:05 Image Super-Resolution with Deep Variational Autoencoders (# 341 )
Darius Chira (DTU)*; Ilian O Haralampiev (DTU); Ole Winther (DTU and KU); Andrea Dittadi (Technical University of Denmark); Valentin V.D.J. Liévin (Technical University of Denmark)
[12:10 UTC] 15:10 XCAT – Lightweight Quantized Single Image Super-Resolution Using Heterogeneous Group Convolutions and Cross Concatenation (# 346 )
Mustafa Ayazoglu (Aselsan Research)*; Bahri Batuhan Bilecen (Aselsan Research)
[12:15 UTC] 15:15 RCBSR: Re-Parameterization Convolution Block for Super-Resolution (# 350 )
Si Gao (State Key Laboratory of Mobile Network and Mobile Multimedia Technology, ZTE Corporation); Chengjian Zheng (State Key Laboratory of Mobile Network and Mobile Multimedia Technology, ZTE Corporation)*; Xiaofeng zhang (State Key Laboratory of Mobile Network and Mobile Multimedia Technology, ZTE Corporation); shaoli liu (State Key Laboratory of Mobile Network and Mobile Multimedia Technology, ZTE Corporation); Biao Wu (State Key Laboratory of Mobile Network and Mobile Multimedia Technology, ZTE Corporation); Kaidi Lu (State Key Laboratory of Mobile Network and Mobile Multimedia Technology, ZTE Corporation); Diankai Zhang (State Key Laboratory of Mobile Network and Mobile Multimedia Technology, ZTE Corporation); Ning Wang (State Key Laboratory of Mobile Network and Mobile Multimedia Technology,ZTE Corporation)
[12:20 UTC] 15:20 Unified Transformer Network for Multi-Weather Image Restoration (# 338 )
Ashutosh C Kulkarni (Indian Institute of Technology, Ropar)*; Shruti S Phutke (Indian Institute of Technology Ropar); Subrahmanyam Murala (IIT Ropar)
[12:25 UTC] 15:25 MSSNet: Multi-Scale-Stage Network for Single Image Deblurring (# 349 )
Kiyeon Kim (POSTECH); Seungyong Lee (POSTECH); Sunghyun Cho (POSTECH)*
[12:30 UTC] 15:30 Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report (# 368 )
Andrey Ignatov (ETH Zurich)*; Radu Timofte (University of Wurzburg & ETH Zurich); Cheng-Ming Chiang (MediaTek Inc.); Hsien-Kai Kuo (MediaTek); Yu-Syuan Xu (MediaTek); My Lee (MediaTek); Allen Lu (MediaTek); Chia-Ming Cheng (MediaTek); Chih-Cheng Chen (MediaTek); Jia-Ying Yong (MediaTek); Hong-Han Shuai (National Yang Ming Chiao Tung University); Wen-Huang Cheng (National Yang Ming Chiao Tung University) et al.
[12:40 UTC] 15:40 Sliding Window Recurrent Network for Efficient Video Super-Resolution (# 354 )
Wenyi Lian (Uppsala University)*; Wenjing Lian (Northeastern University)
[12:45 UTC] 15:45 EESRNet: A Network for Energy Efficient Super-Resolution (# 355 )
Shijie Yue (North China University of Technology)*; Chenghua Li (Institute of Automation Chinese Academy of Sciences); Zhengyang Zhuge ( Institute of Automation, Chinese Academy of Sciences ); Ruixia Song (North China University of Technology)
[12:50 UTC] 15:50 Light Field Angular Super-Resolution via Dense Correspondence Field Reconstruction (# 342 )
Yu Mo (National University of Defense Technology); Yingqian Wang (National University of Defense Technology )*; Longguang Wang (National University of Defense Technology); Jungang Yang (National University of Defense Technology); Wei An (National University of Defense Technology)
[12:55 UTC] 15:55 Towards Real-World Video Deblurring by Exploring Blur Formation Process (# 337 )
Mingdeng Cao (Tsinghua University); Zhihang Zhong (The University of Tokyo); Yanbo Fan (Tencent AI Lab); Jiahao Wang (Tsinghua University); Yong Zhang (Tencent AI Lab); Jue Wang (Tencent AI Lab); Yujiu Yang (Tsinghua University)*; Yinqiang Zheng (The University of Tokyo)
[13:30 UTC] 16:30 AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results (# 371 )
Ren Yang (ETH Zurich)*; Radu Timofte (University of Wurzburg & ETH Zurich) et al.
[13:40 UTC] 16:40 CIDBNet: A Consecutively-Interactive Dual-Branch Network for JPEG Compressed Image Super-Resolution (# 345 )
Xiaoran Qin (Institute of Automation, Chinese Academy of Sciences); Yu Zhu (School of Computer Science and Engineering,Anhui University); Chenghua Li (Institute of Automation Chinese Academy of Sciences)*; Peisong Wang (Institute of Automation, Chinese Academy of Sciences); Jian Cheng ("Chinese Academy of Sciences, China")
[13:45 UTC] 16:45 HST: Hierarchical Swin Transformer for Compressed Image Super-Resolution (# 358 )
Bingchen Li (Unversity of Science and Technology of China); Xin Li (University of Science and Technology of China); yiting lu (University of Science and Technology of China); Sen Liu (University of Science and Technology of China); Ruoyu Feng (University of Science and Technology of China); Zhibo Chen (University of Science and Technology of China)*
[13:50 UTC] 16:50 Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration (# 359 )
Marcos V. Conde (University of Würzburg)*; CHOI Ui-Jin (MegastudyEdu); Maxime Burchi (University of Würzburg); Radu Timofte (University of Wurzburg & ETH Zurich)
[14:30 UTC] 17:30 AIM 2022 Challenge on Instagram Filter Removal: Methods and Results (# 364 )
Furkan Osman Kınlı (Özyeğin University)*; Sami Menteş (Ozyegin University); Barış Özcan (Özyeğin University); Furkan Kirac (Ozyegin University); Radu Timofte (University of Wurzburg & ETH Zurich) et al.
[14:40 UTC] 17:40 CAIR: Fast and Lightweight Multi-Scale Color Attention Network for Instagram Filter Removal (# 362 )
Woon-Ha Yeo (Sahmyook University)*; Wang-Taek Oh (Sahmyook University); Kyung-Su Kang (Sahmyook University); Young-Il Kim (Sahmyook University); Han-Cheol Ryu (Sahmyook University)
[14:45 UTC] 17:45 Unsupervised Scene Sketch to Photo Synthesis (# 334 )
Jiayun Wang (UC Berkeley / ICSI)*; Sangryul Jeon (UC Berkeley); Stella X Yu (UC Berkeley / ICSI); Xi Zhang (Amazon); Himanshu Arora (Amazon); Yu Lou (Amazon)