Publications (379)
Deep Unfolding Network for Image Super-Resolution
Kai Zhang, Luc Van Gool, Radu Timofte
Learning-based single image super-resolution (SISR) methods are continuously showing superior effectiveness and efficiency over traditional model-based methods, largely due to the…
AI Benchmark: Running Deep Neural Networks on Android Smartphones
Andrey Ignatov, Radu Timofte, William Chou +4
Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ag…
The Tenth NTIRE 2025 Image Denoising Challenge Report
Lei Sun, Hang Guo, Bin Ren +91
This paper presents an overview of the NTIRE 2025 Image Denoising Challenge (Ï = 50), highlighting the proposed methodologies and corresponding results. The primary objective is t…
Enhancing LLM-Based Neural Network Generation: Few-Shot Prompting and Efficient Validation for Automated Architecture Design
Raghuvir Duvvuri, Chandini Vysyaraju, Avi Goyal +2
Automated neural network architecture design remains a significant challenge in computer vision. Task diversity and computational constraints require both effective architectures a…
NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results
Xiaoning Liu, Zongwei Wu, Florin-Alexandru Vasluianu +102
This paper presents a comprehensive review of the NTIRE 2025 Low-Light Image Enhancement (LLIE) Challenge, highlighting the proposed solutions and final outcomes. The objective of…
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study
Xin Li, Xijun Wang, Bingchen Li +7
In this work, we build the first benchmark dataset for short-form UGC Image Super-resolution in the wild, termed KwaiSR, intending to advance the research on developing image super…
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
Andrey Ignatov, Nikolay Kobyshev, Radu Timofte +2
Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations - small sensor size, compact lenses and the lack of specific hardware, - impede them…
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results
Dario Fuoli, Zhiwu Huang, Shuhang Gu +23
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)…
Single Image Super Resolution - When Model Adaptation Matters
Yudong Liang, Radu Timofte, Jinjun Wang +2
In the recent years impressive advances were made for single image super-resolution. Deep learning is behind a big part of this success. Deep(er) architecture design and external p…
Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model
Gregor Geigle, Florian Schneider, Carolin Holtermann +4
Most Large Vision-Language Models (LVLMs) to date are trained predominantly on English data, which makes them struggle to understand non-English input and fail to generate output i…
AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content
Marcos V Conde, Zhijun Lei, Wen Li +3
Video super-resolution (VSR) is a critical task for enhancing low-bitrate and low-resolution videos, particularly in streaming applications. While numerous solutions have been deve…
WaveHiT-SR: Hierarchical Wavelet Network for Efficient Image Super-Resolution
Fayaz Ali, Muhammad Zawish, Steven Davy +1
Transformers have demonstrated promising performance in computer vision tasks, including image super-resolution (SR). The quadratic computational complexity of window self-attentio…
Fast Few-Shot Classification by Few-Iteration Meta-Learning
Ardhendu Shekhar Tripathi, Martin Danelljan, Luc Van Gool +1
Autonomous agents interacting with the real world need to learn new concepts efficiently and reliably. This requires learning in a low-data regime, which is a highly challenging pr…
Efficient and Explicit Modelling of Image Hierarchies for Image Restoration
Yawei Li, Yuchen Fan, Xiaoyu Xiang +4
The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration. To achieve tha…
MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results
Yuekun Dai, Dafeng Zhang, Xiaoming Li +60
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algor…
NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results
Nikolay Safonov, Alexey Bryncev, Andrey Moskalenko +28
This paper presents an overview of the NTIRE 2025 Challenge on UGC Video Enhancement. The challenge constructed a set of 150 user-generated content videos without reference ground…
AIM 2024 Challenge on UHD Blind Photo Quality Assessment
Vlad Hosu, Marcos V. Conde, Lorenzo Agnolucci +3
We introduce the AIM 2024 UHD-IQA Challenge, a competition to advance the No-Reference Image Quality Assessment (NR-IQA) task for modern, high-resolution photos. The challenge is b…
AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results
Ivan Molodetskikh, Artem Borisov, Dmitriy Vatolin +24
This paper presents the Video Super-Resolution (SR) Quality Assessment (QA) Challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with E…
SciDef: Datasets and Tools for Automated Definition Extraction from Scientific Literature with LLMs
Filip KuÄera, Christoph Mandl, Isao Echizen +2
Scientific concepts are often defined inconsistently across papers, making it difficult to compare findings, reuse terminology, and build reliable downstream resources. We present…
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
Yawei Li, Shuhang Gu, Christoph Mayer +2
In this paper, we analyze two popular network compression techniques, i.e. filter pruning and low-rank decomposition, in a unified sense. By simply changing the way the sparsity re…
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report
Andrei Dumitriu, Florin Miron, Florin Tatui +24
This report presents an overview of the AIM 2025 RipSeg Challenge, a competition designed to advance techniques for automatic rip current segmentation in still images. Rip currents…
Systematic Exploration of 4-Expert Heterogeneous Mixture-of-Experts via Automated Pipeline Search
Yashkumar R Lukhi, Harsh Rameshbhai Moradiya, Radu Timofte +1
We present an automated large-scale search pipeline for heterogeneous 4-Expert Mixture-of-Experts (MoE4) architectures within the LEMUR neural network dataset ecosystem. Building o…
From Code to Prediction: Fine-Tuning LLMs for Neural Network Performance Classification in NNGPT
Mahmoud Hanouneh, Radu Timofte, Dmitry Ignatov
Automated Machine Learning (AutoML) frameworks increasingly leverage Large Language Models (LLMs) for tasks such as hyperparameter optimization and neural architecture code generat…
Efficient Degradation-aware Any Image Restoration
Eduard Zamfir, Zongwei Wu, Nancy Mehta +3
Reconstructing missing details from degraded low-quality inputs poses a significant challenge. Recent progress in image restoration has demonstrated the efficacy of learning large…
NTIRE 2021 Challenge on Image Deblurring
Seungjun Nah, Sanghyun Son, Suyoung Lee +2
Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge o…
AIM 2020: Scene Relighting and Illumination Estimation Challenge
Majed El Helou, Ruofan Zhou, Sabine Süsstrunk +34
We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different propos…
NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
Dario Fuoli, Zhiwu Huang, Martin Danelljan +18
This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain. The chall…
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
Shanxin Yuan, Radu Timofte, Ales Leonardis +43
This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demo…
Sequence-Adaptive Video Prediction in Continuous Streams using Diffusion Noise Optimization
Sina Mokhtarzadeh Azar, Emad Bahrami, Enrico Pallotta +3
In this work, we investigate diffusion-based video prediction models, which forecast future video frames, for continuous video streams. In this context, the models observe continuo…
InstructIR: High-Quality Image Restoration Following Human Instructions
Marcos V. Conde, Gregor Geigle, Radu Timofte
Image restoration is a fundamental problem that involves recovering a high-quality clean image from its degraded observation. All-In-One image restoration models can effectively re…
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
Zixiang Zhao, Haowen Bai, Yuanzhi Zhu +7
Multi-modality image fusion aims to combine different modalities to produce fused images that retain the complementary features of each modality, such as functional highlights and…
LEMUR Neural Network Dataset: Towards Seamless AutoML
Arash Torabi Goodarzi, Roman Kochnev, Waleed Khalid +8
Neural networks are the backbone of modern artificial intelligence, but designing, evaluating, and comparing them remains labor-intensive. While numerous datasets exist for trainin…
The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
Kai Liu, Haoyang Yue, Zeli Lin +65
This paper presents the NTIRE 2026 Remote Sensing Infrared Image Super-Resolution (x4) Challenge, one of the associated challenges of NTIRE 2026. The challenge aims to recover high…
DiTVR: Zero-Shot Diffusion Transformer for Video Restoration
Sicheng Gao, Nancy Mehta, Zongwei Wu +1
Video restoration aims to reconstruct high quality video sequences from low quality inputs, addressing tasks such as super resolution, denoising, and deblurring. Traditional regres…
Color Matching Using Hypernetwork-Based Kolmogorov-Arnold Networks
Artem Nikonorov, Georgy Perevozchikov, Andrei Korepanov +4
We present cmKAN, a versatile framework for color matching. Given an input image with colors from a source color distribution, our method effectively and accurately maps these colo…
An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement
Dario Fuoli, Zhiwu Huang, Danda Pani Paudel +2
Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the…
Advancing Learned Video Compression with In-loop Frame Prediction
Ren Yang, Radu Timofte, Luc Van Gool
Recent years have witnessed an increasing interest in end-to-end learned video compression. Most previous works explore temporal redundancy by detecting and compressing a motion ma…
Adversarial Feature Distribution Alignment for Semi-Supervised Learning
Christoph Mayer, Matthieu Paul, Radu Timofte
Training deep neural networks with only a few labeled samples can lead to overfitting. This is problematic in semi-supervised learning where only a few labeled samples are availabl…
Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results
Andrei Dumitriu, Florin Tatui, Florin Miron +2
Rip currents are the leading cause of fatal accidents and injuries on many beaches worldwide, emphasizing the importance of automatically detecting these hazardous surface water cu…
Real Image Denoising with Knowledge Distillation for High-Performance Mobile NPUs
Faraz Kayani, Sarmad Kayani, Asad Ahmed +2
While deep-learning-based image restoration has achieved unprecedented fidelity, deployment on mobile Neural Processing Units (NPUs) remains bottlenecked by operator incompatibilit…
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
Andreas Lugmayr, Martin Danelljan, Luc Van Gool +1
Super-resolution is an ill-posed problem, since it allows for multiple predictions for a given low-resolution image. This fundamental fact is largely ignored by state-of-the-art de…
VRT: A Video Restoration Transformer
Jingyun Liang, Jiezhang Cao, Yuchen Fan +5
Video restoration (e.g., video super-resolution) aims to restore high-quality frames from low-quality frames. Different from single image restoration, video restoration generally r…
e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings
Haonan Chen, Sicheng Gao, Radu Timofte +2
Modern information systems often involve different types of items, e.g., a text query, an image, a video clip, or an audio segment. This motivates omni-modal embedding models that…
ContextFormer: Redefining Efficiency in Semantic Segmentation
Mian Muhammad Naeem Abid, Nancy Mehta, Zongwei Wu +1
Semantic segmentation assigns labels to pixels in images, a critical yet challenging task in computer vision. Convolutional methods, although capturing local dependencies well, str…
NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report
Andrei Dumitriu, Aakash Ralhan, Florin Miron +32
This report presents the NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge, which targets automatic rip current understanding in images. Rip currents are haza…
Modulate and Reconstruct: Learning Hyperspectral Imaging from Misaligned Smartphone Views
Daniil Reutsky, Daniil Vladimirov, Yasin Mamedov +4
Hyperspectral reconstruction (HSR) from RGB images is a highly promising direction for accurate color reproduction and material color measurement. While most existing approaches re…
Degradation-Aware All-in-One Image Restoration via Latent Prior Encoding
S M A Sharif, Abdur Rehman, Fayaz Ali Dharejo +2
Real-world images often suffer from spatially diverse degradations such as haze, rain, snow, and low-light, significantly impacting visual quality and downstream vision tasks. Exis…
NTIRE 2021 Depth Guided Image Relighting Challenge
Majed El Helou, Ruofan Zhou, Sabine Susstrunk +1
Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalizati…
NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild
Aleksandr Gushchin, Khaled Abud, Ekaterina Shumitskaya +51
This paper presents an overview of the NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild, held in conjunction with the NTIRE workshop at CVPR 2026. The goal o…
Transform your Smartphone into a DSLR Camera: Learning the ISP in the Wild
Ardhendu Shekhar Tripathi, Martin Danelljan, Samarth Shukla +2
We propose a trainable Image Signal Processing (ISP) framework that produces DSLR quality images given RAW images captured by a smartphone. To address the color misalignments betwe…
Generic 3D Convolutional Fusion for image restoration
Jiqing Wu, Radu Timofte, Luc Van Gool
Also recently, exciting strides forward have been made in the area of image restoration, particularly for image denoising and single image super-resolution. Deep learning technique…
NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results
Andrey Moskalenko, Alexey Bryncev, Ivan Kosmynin +40
This paper presents an overview of the NTIRE 2026 Challenge on Video Saliency Prediction. The goal of the challenge participants was to develop automatic saliency map prediction me…
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement
Yuanhao Cai, Hao Bian, Jing Lin +3
When enhancing low-light images, many deep learning algorithms are based on the Retinex theory. However, the Retinex model does not consider the corruptions hidden in the dark or i…
NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images
Codruta O. Ancuti, Cosmin Ancuti, Radu Timofte
Image dehazing is an ill-posed problem that has been extensively studied in the recent years. The objective performance evaluation of the dehazing methods is one of the major obsta…
LLM as a Neural Architect: Controlled Generation of Image Captioning Models Under Strict API Contracts
Krunal Jesani, Dmitry Ignatov, Radu Timofte
Neural architecture search (NAS) traditionally requires significant human expertise or automated trial-and-error to design deep learning models. We present NN-Caption, an LLM-guide…
Event-Free Moving Object Segmentation from Moving Ego Vehicle
Zhuyun Zhou, Zongwei Wu, Danda Pani Paudel +5
Moving object segmentation (MOS) in dynamic scenes is an important, challenging, but under-explored research topic for autonomous driving, especially for sequences obtained from mo…
NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results
Lei Sun, Andrea Alfarano, Peiqi Duan +85
This paper presents an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results. The primary goal…
Conformer and Blind Noisy Students for Improved Image Quality Assessment
Marcos V. Conde, Maxime Burchi, Radu Timofte
Generative models for image restoration, enhancement, and generation have significantly improved the quality of the generated images. Surprisingly, these models produce more pleasa…
The Regularizing Power of Language-Training Deepfake Detectors
Benedikt Hopf, Zongwei Wu, Radu Timofte
Recently, thanks to the advent of Multimodal-LLMs, deepfake detectors are striving not only to be generalizable but also interpretable. We propose that these two challenges can eff…
O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images
Codruta O. Ancuti, Cosmin Ancuti, Radu Timofte +1
Haze removal or dehazing is a challenging ill-posed problem that has drawn a significant attention in the last few years. Despite this growing interest, the scientific community is…
How to Train Your Energy-Based Model for Regression
Fredrik K. Gustafsson, Martin Danelljan, Radu Timofte +1
Energy-based models (EBMs) have become increasingly popular within computer vision in recent years. While they are commonly employed for generative image modeling, recent work has…
Seven ways to improve example-based single image super resolution
Radu Timofte, Rasmus Rothe, Luc Van Gool
In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionar…
DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation
Alexandre Carlier, Martin Danelljan, Alexandre Alahi +1
Scalable Vector Graphics (SVG) are ubiquitous in modern 2D interfaces due to their ability to scale to different resolutions. However, despite the success of deep learning-based mo…
RePaint: Inpainting using Denoising Diffusion Probabilistic Models
Andreas Lugmayr, Martin Danelljan, Andres Romero +3
Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution o…
Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation
Samarth Shukla, Luc Van Gool, Radu Timofte
Recent advances in generative models and adversarial training have led to a flourishing image-to-image (I2I) translation literature. The current I2I translation approaches require…
Device-First Feedback: Toward Mobile-Native LLM-Driven Neural Architecture Search
Saif U Din, Muhammad Ahsan Hussain, Radu Timofte +1
Deploying convolutional neural networks generated by large language models (LLMs) on real mobile hardware requires more than GPU validation accuracy: INT8 TensorFlow Lite export, d…
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
Eirikur Agustsson, Fabian Mentzer, Michael Tschannen +4
We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of…
Accurate and Efficient World Modeling with Masked Latent Transformers
Maxime Burchi, Radu Timofte
The Dreamer algorithm has recently obtained remarkable performance across diverse environment domains by training powerful agents with simulated trajectories. However, the compress…
mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs
Gregor Geigle, Abhay Jain, Radu Timofte +1
Modular vision-language models (Vision-LLMs) align pretrained image encoders with (frozen) large language models (LLMs) and post-hoc condition LLMs to `understand' the image input.…
Learning to Improve Image Compression without Changing the Standard Decoder
Yannick Strümpler, Ren Yang, Radu Timofte
In recent years we have witnessed an increasing interest in applying Deep Neural Networks (DNNs) to improve the rate-distortion performance in image compression. However, the exist…
AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results
Chao Wang, Francesco Banterle, Bin Ren +25
This paper presents a comprehensive review of the AIM 2025 Challenge on Inverse Tone Mapping (ITM). The challenge aimed to push forward the development of effective ITM algorithms…
AIM 2025 Low-light RAW Video Denoising Challenge: Dataset, Methods and Results
Alexander Yakovenko, George Chakvetadze, Ilya Khrapov +17
This paper reviews the AIM 2025 (Advances in Image Manipulation) Low-Light RAW Video Denoising Challenge. The task is to develop methods that denoise low-light RAW video by exploit…
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
Ren Yang, Radu Timofte, Xin Li +49
This paper reviews the Challenge on Super-Resolution of Compressed Image and Video at AIM 2022. This challenge includes two tracks. Track 1 aims at the super-resolution of compress…
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Andrey Ignatov, Grigory Malivenko, Radu Timofte +36
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus…
Flexible Example-based Image Enhancement with Task Adaptive Global Feature Self-Guided Network
Dario Kneubuehler, Shuhang Gu, Luc Van Gool +1
We propose the first practical multitask image enhancement network, that is able to learn one-to-many and many-to-one image mappings. We show that our model outperforms the current…
NTIRE 2022 Challenge on Perceptual Image Quality Assessment
Jinjin Gu, Haoming Cai, Chao Dong +2
This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (…
African or European Swallow? Benchmarking Large Vision-Language Models for Fine-Grained Object Classification
Gregor Geigle, Radu Timofte, Goran Glavaš
Recent Large Vision-Language Models (LVLMs) demonstrate impressive abilities on numerous image understanding and reasoning tasks. The task of fine-grained object classification (e.…
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
Andrey Ignatov, Radu Timofte, Maurizio Denna +93
Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions hav…
Efficient Perceptual Image Super Resolution: AIM 2025 Study and Benchmark
Bruno Longarela, Marcos V. Conde, Alvaro Garcia +1
This paper presents a comprehensive study and benchmark on Efficient Perceptual Super-Resolution (EPSR). While significant progress has been made in efficient PSNR-oriented super r…
From Memorization to Creativity: LLM as a Designer of Novel Neural Architectures
Waleed Khalid, Dmitry Ignatov, Radu Timofte
Large language models (LLMs) excel in program synthesis, yet their capacity for neural architecture design -- balancing syntactic reliability, performance, and structural novelty -…
NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
Yuqian Fu, Xingyu Qiu, Bin Ren +59
Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection and few-shot detection models when applied across domains. In conjunction…
What You Have is What You Track: Adaptive and Robust Multimodal Tracking
Yuedong Tan, Jiawei Shao, Eduard Zamfir +7
Multimodal data is known to be helpful for visual tracking by improving robustness to appearance variations. However, sensor synchronization challenges often compromise data availa…
OpenDVC: An Open Source Implementation of the DVC Video Compression Method
Ren Yang, Luc Van Gool, Radu Timofte
We introduce an open source Tensorflow implementation of the Deep Video Compression (DVC) method in this technical report. DVC is the first end-to-end optimized learned video compr…
Optimal transport maps for distribution preserving operations on latent spaces of Generative Models
Eirikur Agustsson, Alexander Sage, Radu Timofte +1
Generative models such as Variational Auto Encoders (VAEs) and Generative Adversarial Networks (GANs) are typically trained for a fixed prior distribution in the latent space, such…
GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
Prune Truong, Martin Danelljan, Radu Timofte
Establishing dense correspondences between a pair of images is an important and general problem, covering geometric matching, optical flow and semantic correspondences. While these…
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution
Yan Wu, Zhiwu Huang, Suryansh Kumar +3
Modern solutions to the single image super-resolution (SISR) problem using deep neural networks aim not only at better performance accuracy but also at a lighter and computationall…
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey
Marcos V. Conde, Zhijun Lei, Wen Li +72
This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolutio…
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
Marcos V. Conde, Ui-Jin Choi, Maxime Burchi +1
Compression plays an important role on the efficient transmission and storage of images and videos through band-limited systems such as streaming services, virtual reality or video…
Learning Context-Based Non-local Entropy Modeling for Image Compression
Mu Li, Kai Zhang, Wangmeng Zuo +2
The entropy of the codes usually serves as the rate loss in the recent learned lossy image compression methods. Precise estimation of the probabilistic distribution of the codes pl…
Learning Transformer-based World Models with Contrastive Predictive Coding
Maxime Burchi, Radu Timofte
The DreamerV3 algorithm recently obtained remarkable performance across diverse environment domains by learning an accurate world model based on Recurrent Neural Networks (RNNs). F…
Video Super Resolution Based on Deep Learning: A Comprehensive Survey
Hongying Liu, Zhubo Ruan, Peng Zhao +5
In recent years, deep learning has made great progress in many fields such as image recognition, natural language processing, speech recognition and video super-resolution. In this…
NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results
Yingqian Wang, Longguang Wang, Zhengyu Liang +3
In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradati…
AIM 2020 Challenge on Image Extreme Inpainting
Evangelos Ntavelis, Andrés Romero, Siavash Bigdeli +1
This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: c…
SMIT: Stochastic Multi-Label Image-to-Image Translation
Andrés Romero, Pablo Arbeláez, Luc Van Gool +1
Cross-domain mapping has been a very active topic in recent years. Given one image, its main purpose is to translate it to the desired target domain, or multiple domains in the cas…
Practical Manipulation Model for Robust Deepfake Detection
Benedikt Hopf, Radu Timofte
Modern deepfake detection models have achieved strong performance even on the challenging cross-dataset task. However, detection performance under non-ideal conditions remains very…
NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
Xin Li, Yeying Jin, Xin Jin +134
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images. This challenge received a wide range of impressive solutions, which are devel…
Perceptual Learned Video Compression with Recurrent Conditional GAN
Ren Yang, Radu Timofte, Luc Van Gool
This paper proposes a Perceptual Learned Video Compression (PLVC) approach with recurrent conditional GAN. We employ the recurrent auto-encoder-based compression network as the gen…
AIM 2024 Sparse Neural Rendering Challenge: Methods and Results
Michal Nazarczuk, Sibi Catley-Chandar, Thomas Tanay +27
This paper reviews the challenge on Sparse Neural Rendering that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. This manuscript…
On the Relation between Color Image Denoising and Classification
Jiqing Wu, Radu Timofte, Zhiwu Huang +1
Large amount of image denoising literature focuses on single channel images and often experimentally validates the proposed methods on tens of images at most. In this paper, we inv…
MIORe & VAR-MIORe: Benchmarks to Push the Boundaries of Restoration
George Ciubotariu, Zhuyun Zhou, Zongwei Wu +1
We introduce MIORe and VAR-MIORe, two novel multi-task datasets that address critical limitations in current motion restoration benchmarks. Designed with high-frame-rate (1000 FPS)…