Search Results for author: Radu Timofte

Found 227 papers, 139 papers with code

Empowering Image Recovery_ A Multi-Attention Approach

no code implementations6 Apr 2024 Juan Wen, Yawei Li, Chao Zhang, Weiyan Hou, Radu Timofte, Luc van Gool

Integration of attention mechanisms across feature and positional dimensions further enhances the recovery of fine details.

Image Restoration

Towards Image Ambient Lighting Normalization

no code implementations27 Mar 2024 Florin-Alexandru Vasluianu, Tim Seizinger, Zongwei Wu, Rakesh Ranjan, Radu Timofte

However, existing works often simplify this task within the context of shadow removal, limiting the light sources to one and oversimplifying the scene, thus excluding complex self-shadows and restricting surface classes to smooth ones.

Benchmarking Image Restoration +1

Towards Online Real-Time Memory-based Video Inpainting Transformers

no code implementations24 Mar 2024 Guillaume Thiry, Hao Tang, Radu Timofte, Luc van Gool

Video inpainting tasks have seen significant improvements in recent years with the rise of deep neural networks and, in particular, vision transformers.

Video Inpainting

See More Details: Efficient Image Super-Resolution by Experts Mining

no code implementations5 Feb 2024 Eduard Zamfir, Zongwei Wu, Nancy Mehta, Yulun Zhang, Radu Timofte

Subsequently, the model delves into the subtleties of rank choice by leveraging a mixture of low-rank experts.

Image Super-Resolution

InstructIR: High-Quality Image Restoration Following Human Instructions

1 code implementation29 Jan 2024 Marcos V. Conde, Gregor Geigle, Radu Timofte

All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model.

Deblurring Image Denoising +4

BSRAW: Improving Blind RAW Image Super-Resolution

1 code implementation24 Dec 2023 Marcos V. Conde, Florin Vasluianu, Radu Timofte

Our BSRAW models trained with our pipeline can upscale real-scene RAW images and improve their quality.

Image Super-Resolution

Single-Model and Any-Modality for Video Object Tracking

1 code implementation27 Nov 2023 Zongwei Wu, Jilai Zheng, Xiangxuan Ren, Florin-Alexandru Vasluianu, Chao Ma, Danda Pani Paudel, Luc van Gool, Radu Timofte

In practice, most existing RGB trackers learn a single set of parameters to use them across datasets and applications.

Object Video Object Tracking

Deep Equilibrium Diffusion Restoration with Parallel Sampling

1 code implementation20 Nov 2023 JieZhang Cao, Yue Shi, Kai Zhang, Yulun Zhang, Radu Timofte, Luc van Gool

Due to the inherent property of diffusion models, most existing methods need long serial sampling chains to restore HQ images step-by-step, resulting in expensive sampling time and high computation costs.

Image Restoration

MoVideo: Motion-Aware Video Generation with Diffusion Models

no code implementations19 Nov 2023 Jingyun Liang, Yuchen Fan, Kai Zhang, Radu Timofte, Luc van Gool, Rakesh Ranjan

While recent years have witnessed great progress on using diffusion models for video generation, most of them are simple extensions of image generation frameworks, which fail to explicitly consider one of the key differences between videos and images, i. e., motion.

Image Generation Image to Video Generation +1

A Study of Forward-Forward Algorithm for Self-Supervised Learning

no code implementations21 Sep 2023 Jonas Brenig, Radu Timofte

This may be caused by a combination of factors, including having a loss function for each layer and the way the supervised training is realized in the forward-forward paradigm.

Representation Learning Self-Supervised Learning

Neural Gradient Regularizer

1 code implementation31 Aug 2023 Shuang Xu, Yifan Wang, Zixiang Zhao, Jiangjun Peng, Xiangyong Cao, Deyu Meng, Yulun Zhang, Radu Timofte, Luc van Gool

NGR is applicable to various image types and different image processing tasks, functioning in a zero-shot learning fashion, making it a versatile and plug-and-play regularizer.

Zero-Shot Learning

When Super-Resolution Meets Camouflaged Object Detection: A Comparison Study

no code implementations8 Aug 2023 Juan Wen, Shupeng Cheng, Peng Xu, BoWen Zhou, Radu Timofte, Weiyan Hou, Luc van Gool

Super Resolution (SR) and Camouflaged Object Detection (COD) are two hot topics in computer vision with various joint applications.

Object object-detection +2

mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs

1 code implementation13 Jul 2023 Gregor Geigle, Abhay Jain, Radu Timofte, Goran Glavaš

To this end, we \textit{re-align} an image encoder previously tuned to an English LLM to a new, multilingual LLM -- for this, we leverage multilingual data from a mix of vision-and-language tasks, which we obtain by machine-translating high-quality English data to 95 languages.

Image Captioning

NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement

1 code implementation20 Jun 2023 Marcos V. Conde, Javier Vazquez-Corral, Michael S. Brown, Radu Timofte

Moreover, a NILUT can be extended to incorporate multiple styles into a single network with the ability to blend styles implicitly.

Color Manipulation Photo Retouching +1

Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations

1 code implementation14 Jun 2023 Gregor Geigle, Radu Timofte, Goran Glavaš

We evaluate 8 different publicly available multilingual CLIP models on zero-shot image classification (ZS-IC) for each of the 92 Babel-ImageNet languages, demonstrating a significant gap between English ImageNet performance and that of high-resource languages (e. g., German or Chinese), and an even bigger gap for low-resource languages (e. g., Sinhala or Lao).

Image Classification Machine Translation +3

Efficient multi-lens bokeh effect rendering and transformation

1 code implementation CVPR 2023 Tim Seizinger, Marcos V. Conde, Manuel Kolmet, Tom E. Bishop, Radu Timofte

Our method can render Bokeh from an all-in-focus image, or transform the Bokeh of one lens to the effect of another lens without harming the sharp foreground regions in the image.

Bokeh Effect Rendering

Towards Real-Time 4K Image Super-Resolution

2 code implementations CVPRW 2023 Eduard Zamfir, Marcos V. Conde, Radu Timofte

Over the past few years, high-definition videos and images in 720p (HD), 1080p (FHD), and 4K (UHD) resolution have become standard.

4k Image Super-Resolution

Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report

1 code implementation CVPRW 2023 Marcos V. Conde, Eduard Zamfir, Radu Timofte, Daniel Motilla, and others

This paper introduces a novel benchmark for efficient upscaling as part of the NTIRE 2023 Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images from 720p and 1080p resolution to native 4K (x2 and x3 factors) in real-time on commercial GPUs.

4k Image Super-Resolution

Alignment-free HDR Deghosting with Semantics Consistent Transformer

no code implementations ICCV 2023 Steven Tel, Zongwei Wu, Yulun Zhang, Barthélémy Heyrman, Cédric Demonceaux, Radu Timofte, Dominique Ginhac

The spatial attention aims to deal with the intra-image correlation to model the dynamic motion, while the channel attention enables the inter-image intertwining to enhance the semantic consistency across frames.

Image Generation

Equivariant Multi-Modality Image Fusion

2 code implementations19 May 2023 Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang, Kai Zhang, Shuang Xu, Dongdong Chen, Radu Timofte, Luc van Gool

Multi-modality image fusion is a technique used to combine information from different sensors or modalities, allowing the fused image to retain complementary features from each modality, such as functional highlights and texture details.

Self-Supervised Learning

Object Segmentation by Mining Cross-Modal Semantics

1 code implementation17 May 2023 Zongwei Wu, Jingjing Wang, Zhuyun Zhou, Zhaochong An, Qiuping Jiang, Cédric Demonceaux, Guolei Sun, Radu Timofte

In this paper, we propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features, with the aim of controlling the modal contribution based on relative entropy.

Object Segmentation +2

Denoising Diffusion Models for Plug-and-Play Image Restoration

2 code implementations15 May 2023 Yuanzhi Zhu, Kai Zhang, Jingyun Liang, JieZhang Cao, Bihan Wen, Radu Timofte, Luc van Gool

Although diffusion models have shown impressive performance for high-quality image synthesis, their potential to serve as a generative denoiser prior to the plug-and-play IR methods remains to be further explored.

Deblurring Denoising +4

StyleGenes: Discrete and Efficient Latent Distributions for GANs

no code implementations30 Apr 2023 Evangelos Ntavelis, Mohamad Shahbazi, Iason Kastanis, Radu Timofte, Martin Danelljan, Luc van Gool

Thus, by independently sampling a variant for each gene and combining them into the final latent vector, our approach can represent a vast number of unique latent samples from a compact set of learnable parameters.


NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

1 code implementation20 Apr 2023 Yingqian Wang, Longguang Wang, Zhengyu Liang, Jungang Yang, Radu Timofte, Yulan Guo

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 degradation with a magnification factor of 4.

Image Super-Resolution

Single Image Depth Prediction Made Better: A Multivariate Gaussian Take

no code implementations CVPR 2023 Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Luc van Gool

Accordingly, we introduce an approach that performs continuous modeling of per-pixel depth, where we can predict and reason about the per-pixel depth and its distribution.

Depth Estimation Depth Prediction

Graph Transformer GANs for Graph-Constrained House Generation

no code implementations CVPR 2023 Hao Tang, Zhenyu Zhang, Humphrey Shi, Bo Li, Ling Shao, Nicu Sebe, Radu Timofte, Luc van Gool

We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task.

Generative Adversarial Network House Generation +1

DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion

2 code implementations ICCV 2023 Zixiang Zhao, Haowen Bai, Yuanzhi Zhu, Jiangshe Zhang, Shuang Xu, Yulun Zhang, Kai Zhang, Deyu Meng, Radu Timofte, Luc van Gool

To leverage strong generative priors and address challenges such as unstable training and lack of interpretability for GAN-based generative methods, we propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM).


Efficient and Explicit Modelling of Image Hierarchies for Image Restoration

1 code implementation CVPR 2023 Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, Luc van Gool

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.

Image Deblurring Image Defocus Deblurring +1

VA-DepthNet: A Variational Approach to Single Image Depth Prediction

2 code implementations13 Feb 2023 Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Luc van Gool

While state-of-the-art deep neural network methods for SIDP learn the scene depth from images in a supervised setting, they often overlook the invaluable invariances and priors in the rigid scene space, such as the regularity of the scene.

Depth Prediction Monocular Depth Estimation

Audio-Visual Efficient Conformer for Robust Speech Recognition

1 code implementation4 Jan 2023 Maxime Burchi, Radu Timofte

We improve previous lip reading methods using an Efficient Conformer back-end on top of a ResNet-18 visual front-end and by adding intermediate CTC losses between blocks.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

SQAD: Automatic Smartphone Camera Quality Assessment and Benchmarking

1 code implementation ICCV 2023 Zilin Fang, Andrey Ignatov, Eduard Zamfir, Radu Timofte

Smartphone photography is becoming increasingly popular, but fitting high-performing camera systems within the given space limitations remains a challenge for manufacturers.


CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion

2 code implementations CVPR 2023 Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang, Shuang Xu, Zudi Lin, Radu Timofte, Luc van Gool

We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle low-frequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information.

object-detection Object Detection +1

Real-Time Under-Display Cameras Image Restoration and HDR on Mobile Devices

1 code implementation25 Nov 2022 Marcos V. Conde, Florin Vasluianu, Sabari Nathan, Radu Timofte

We propose a lightweight model for blind UDC Image Restoration and HDR, and we also provide a benchmark comparing the performance and runtime of different methods on smartphones.

Image Restoration

Advancing Learned Video Compression with In-loop Frame Prediction

1 code implementation13 Nov 2022 Ren Yang, Radu Timofte, Luc van Gool

In this paper, we propose an Advanced Learned Video Compression (ALVC) approach with the in-loop frame prediction module, which is able to effectively predict the target frame from the previously compressed frames, without consuming any bit-rate.


MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning

no code implementations8 Nov 2022 Andrey Ignatov, Anastasia Sycheva, Radu Timofte, Yu Tseng, Yu-Syuan Xu, Po-Hsiang Yu, Cheng-Ming Chiang, Hsien-Kai Kuo, Min-Hung Chen, Chia-Ming Cheng, Luc van Gool

While neural networks-based photo processing solutions can provide a better image quality compared to the traditional ISP systems, their application to mobile devices is still very limited due to their very high computational complexity.

PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks

1 code implementation8 Nov 2022 Andrey Ignatov, Grigory Malivenko, Radu Timofte, Yu Tseng, Yu-Syuan Xu, Po-Hsiang Yu, Cheng-Ming Chiang, Hsien-Kai Kuo, Min-Hung Chen, Chia-Ming Cheng, Luc van Gool

The increased importance of mobile photography created a need for fast and performant RAW image processing pipelines capable of producing good visual results in spite of the mobile camera sensor limitations.

Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

2 code implementations7 Nov 2022 Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Ganzorig Gankhuyag, Jingang Huh, Myeong Kyun Kim, Kihwan Yoon, Hyeon-Cheol Moon, Seungho Lee, Yoonsik Choe, Jinwoo Jeong, Sungjei Kim, Maciej Smyl, Tomasz Latkowski, Pawel Kubik, Michal Sokolski, Yujie Ma, Jiahao Chao, Zhou Zhou, Hongfan Gao, Zhengfeng Yang, Zhenbing Zeng, Zhengyang Zhuge, Chenghua Li, Dan Zhu, Mengdi Sun, Ran Duan, Yan Gao, Lingshun Kong, Long Sun, Xiang Li, Xingdong Zhang, Jiawei Zhang, Yaqi Wu, Jinshan Pan, Gaocheng Yu, Jin Zhang, Feng Zhang, Zhe Ma, Hongbin Wang, Hojin Cho, Steve Kim, Huaen Li, Yanbo Ma, Ziwei Luo, Youwei Li, Lei Yu, Zhihong Wen, Qi Wu, Haoqiang Fan, Shuaicheng Liu, Lize Zhang, Zhikai Zong, Jeremy Kwon, Junxi Zhang, Mengyuan Li, Nianxiang Fu, Guanchen Ding, Han Zhu, Zhenzhong Chen, Gen Li, Yuanfan Zhang, Lei Sun, Dafeng Zhang, Neo Yang, Fitz Liu, Jerry Zhao, Mustafa Ayazoglu, Bahri Batuhan Bilecen, Shota Hirose, Kasidis Arunruangsirilert, Luo Ao, Ho Chun Leung, Andrew Wei, Jie Liu, Qiang Liu, Dahai Yu, Ao Li, Lei Luo, Ce Zhu, Seongmin Hong, Dongwon Park, Joonhee Lee, Byeong Hyun Lee, Seunggyu Lee, Se Young Chun, Ruiyuan He, Xuhao Jiang, Haihang Ruan, Xinjian Zhang, Jing Liu, Garas Gendy, Nabil Sabor, Jingchao Hou, Guanghui He

While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.

Image Super-Resolution

Perceptual Image Enhancement for Smartphone Real-Time Applications

1 code implementation24 Oct 2022 Marcos V. Conde, Florin Vasluianu, Javier Vazquez-Corral, Radu Timofte

Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks.

2k HDR Reconstruction +4

SiNeRF: Sinusoidal Neural Radiance Fields for Joint Pose Estimation and Scene Reconstruction

1 code implementation10 Oct 2022 Yitong Xia, Hao Tang, Radu Timofte, Luc van Gool

NeRFmm is the Neural Radiance Fields (NeRF) that deal with Joint Optimization tasks, i. e., reconstructing real-world scenes and registering camera parameters simultaneously.

Image Generation Pose Estimation

Basic Binary Convolution Unit for Binarized Image Restoration Network

2 code implementations2 Oct 2022 Bin Xia, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Radu Timofte, Luc van Gool

In this study, we reconsider components in binary convolution, such as residual connection, BatchNorm, activation function, and structure, for IR tasks.

Binarization Image Restoration +1

Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration

2 code implementations22 Sep 2022 Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte

Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data.

Compressed Image Super-resolution Image Super-Resolution +1

3D-Aware Video Generation

1 code implementation29 Jun 2022 Sherwin Bahmani, Jeong Joon Park, Despoina Paschalidou, Hao Tang, Gordon Wetzstein, Leonidas Guibas, Luc van Gool, Radu Timofte

Generative models have emerged as an essential building block for many image synthesis and editing tasks.

Image Generation Video Generation

NTIRE 2022 Challenge on Perceptual Image Quality Assessment

no code implementations23 Jun 2022 Jinjin Gu, Haoming Cai, Chao Dong, Jimmy S. Ren, Radu Timofte

This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods.

Image Quality Assessment Image Restoration

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

no code implementations25 May 2022 Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park

The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).

Image Restoration Vocal Bursts Intensity Prediction

Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging

1 code implementation20 May 2022 Yuanhao Cai, Jing Lin, Haoqian Wang, Xin Yuan, Henghui Ding, Yulun Zhang, Radu Timofte, Luc van Gool

In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from a compressed measurement.

Compressive Sensing Image Reconstruction +1

Revisiting Random Channel Pruning for Neural Network Compression

1 code implementation CVPR 2022 Yawei Li, Kamil Adamczewski, Wen Li, Shuhang Gu, Radu Timofte, Luc van Gool

The proposed approach provides a new way to compare different methods, namely how well they behave compared with random pruning.

Neural Network Compression

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

2 code implementations11 May 2022 Yawei Li, Kai Zhang, Radu Timofte, Luc van Gool, Fangyuan Kong, Mingxi Li, Songwei Liu, Zongcai Du, Ding Liu, Chenhui Zhou, Jingyi Chen, Qingrui Han, Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Yu Qiao, Chao Dong, Long Sun, Jinshan Pan, Yi Zhu, Zhikai Zong, Xiaoxiao Liu, Zheng Hui, Tao Yang, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Yanbo Wang, Xiaozhong Ji, Chuming Lin, Donghao Luo, Ying Tai, Chengjie Wang, Zhizhong Zhang, Yuan Xie, Shen Cheng, Ziwei Luo, Lei Yu, Zhihong Wen, Qi Wu1, Youwei Li, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Yuanfei Huang, Meiguang Jin, Hua Huang, Jing Liu, Xinjian Zhang, Yan Wang, Lingshun Long, Gen Li, Yuanfan Zhang, Zuowei Cao, Lei Sun, Panaetov Alexander, Yucong Wang, Minjie Cai, Li Wang, Lu Tian, Zheyuan Wang, Hongbing Ma, Jie Liu, Chao Chen, Yidong Cai, Jie Tang, Gangshan Wu, Weiran Wang, Shirui Huang, Honglei Lu, Huan Liu, Keyan Wang, Jun Chen, Shi Chen, Yuchun Miao, Zimo Huang, Lefei Zhang, Mustafa Ayazoğlu, Wei Xiong, Chengyi Xiong, Fei Wang, Hao Li, Ruimian Wen, Zhijing Yang, Wenbin Zou, Weixin Zheng, Tian Ye, Yuncheng Zhang, Xiangzhen Kong, Aditya Arora, Syed Waqas Zamir, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Dandan Gaoand Dengwen Zhouand Qian Ning, Jingzhu Tang, Han Huang, YuFei Wang, Zhangheng Peng, Haobo Li, Wenxue Guan, Shenghua Gong, Xin Li, Jun Liu, Wanjun Wang, Dengwen Zhou, Kun Zeng, Hanjiang Lin, Xinyu Chen, Jinsheng Fang

The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.

Image Super-Resolution

Conformer and Blind Noisy Students for Improved Image Quality Assessment

1 code implementation27 Apr 2022 Marcos V. Conde, Maxime Burchi, Radu Timofte

Learning-based approaches for perceptual image quality assessment (IQA) usually require both the distorted and reference image for measuring the perceptual quality accurately.

Blind Image Quality Assessment Image Restoration +3

NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results

no code implementations20 Apr 2022 Longguang Wang, Yulan Guo, Yingqian Wang, Juncheng Li, Shuhang Gu, Radu Timofte

In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results.

Stereo Image Super-Resolution

MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction

3 code implementations17 Apr 2022 Yuanhao Cai, Jing Lin, Zudi Lin, Haoqian Wang, Yulun Zhang, Hanspeter Pfister, Radu Timofte, Luc van Gool

Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image to its hyperspectral image (HSI).

Spectral Reconstruction Spectral Super-Resolution

Arbitrary-Scale Image Synthesis

1 code implementation CVPR 2022 Evangelos Ntavelis, Mohamad Shahbazi, Iason Kastanis, Radu Timofte, Martin Danelljan, Luc van Gool

Positional encodings have enabled recent works to train a single adversarial network that can generate images of different scales.

Image Generation

Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

2 code implementations24 Mar 2022 Kai Zhang, Yawei Li, Jingyun Liang, JieZhang Cao, Yulun Zhang, Hao Tang, Deng-Ping Fan, Radu Timofte, Luc van Gool

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved.

Image Denoising Image-to-Image Translation

Transform your Smartphone into a DSLR Camera: Learning the ISP in the Wild

no code implementations20 Mar 2022 Ardhendu Shekhar Tripathi, Martin Danelljan, Samarth Shukla, Radu Timofte, Luc van Gool

We propose a trainable Image Signal Processing (ISP) framework that produces DSLR quality images given RAW images captured by a smartphone.

Motion Estimation

Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction

1 code implementation9 Mar 2022 Yuanhao Cai, Jing Lin, Xiaowan Hu, Haoqian Wang, Xin Yuan, Yulun Zhang, Radu Timofte, Luc van Gool

Many algorithms have been developed to solve the inverse problem of coded aperture snapshot spectral imaging (CASSI), i. e., recovering the 3D hyperspectral images (HSIs) from a 2D compressive measurement.

Compressive Sensing Image Reconstruction +1

Fast Online Video Super-Resolution with Deformable Attention Pyramid

no code implementations3 Feb 2022 Dario Fuoli, Martin Danelljan, Radu Timofte, Luc van Gool

Our DAP aligns and integrates information from the recurrent state into the current frame prediction.

Video Super-Resolution

VRT: A Video Restoration Transformer

1 code implementation28 Jan 2022 Jingyun Liang, JieZhang Cao, Yuchen Fan, Kai Zhang, Rakesh Ranjan, Yawei Li, Radu Timofte, Luc van Gool

Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping.

Deblurring Denoising +7

RePaint: Inpainting using Denoising Diffusion Probabilistic Models

3 code implementations CVPR 2022 Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, Luc van Gool

In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks.

Denoising Image Inpainting

Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model

1 code implementation CVPR 2022 Zipeng Xu, Tianwei Lin, Hao Tang, Fu Li, Dongliang He, Nicu Sebe, Radu Timofte, Luc van Gool, Errui Ding

We propose a novel framework, i. e., Predict, Prevent, and Evaluate (PPE), for disentangled text-driven image manipulation that requires little manual annotation while being applicable to a wide variety of manipulations.

Image Manipulation Language Modelling

Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution

no code implementations5 Nov 2021 Andreas Lugmayr, Martin Danelljan, Fisher Yu, Luc van Gool, Radu Timofte

Super-resolution is an ill-posed problem, where a ground-truth high-resolution image represents only one possibility in the space of plausible solutions.


Towards Flexible Blind JPEG Artifacts Removal

2 code implementations ICCV 2021 Jiaxi Jiang, Kai Zhang, Radu Timofte

Training a single deep blind model to handle different quality factors for JPEG image artifacts removal has been attracting considerable attention due to its convenience for practical usage.

Image Compression Image Compression Artifact Reduction +5

Best Practices in Pool-based Active Learning for Image Classification

no code implementations29 Sep 2021 Adrian Lang, Christoph Mayer, Radu Timofte

We emphasize aspects such as the importance of using data augmentation, the need of separating the contribution of a classification network and the acquisition strategy to the overall performance, the advantages that a proper initialization of the network can bring to AL.

Active Learning Benchmarking +3

PDC-Net+: Enhanced Probabilistic Dense Correspondence Network

1 code implementation28 Sep 2021 Prune Truong, Martin Danelljan, Radu Timofte, Luc van Gool

In order to apply dense methods to real-world applications, such as pose estimation, image manipulation, or 3D reconstruction, it is therefore crucial to estimate the confidence of the predicted matches.

3D Reconstruction Geometric Matching +6

Perceptual Learned Video Compression with Recurrent Conditional GAN

3 code implementations7 Sep 2021 Ren Yang, Radu Timofte, Luc van Gool

This paper proposes a Perceptual Learned Video Compression (PLVC) approach with recurrent conditional GAN.

Video Compression

Generalized Real-World Super-Resolution through Adversarial Robustness

1 code implementation25 Aug 2021 Angela Castillo, María Escobar, Juan C. Pérez, Andrés Romero, Radu Timofte, Luc van Gool, Pablo Arbeláez

Instead of learning a dataset-specific degradation, we employ adversarial attacks to create difficult examples that target the model's weaknesses.

Adversarial Robustness Super-Resolution

SwinIR: Image Restoration Using Swin Transformer

9 code implementations23 Aug 2021 Jingyun Liang, JieZhang Cao, Guolei Sun, Kai Zhang, Luc van Gool, Radu Timofte

In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.

Color Image Denoising Grayscale Image Denoising +6

Deep Reparametrization of Multi-Frame Super-Resolution and Denoising

2 code implementations ICCV 2021 Goutam Bhat, Martin Danelljan, Fisher Yu, Luc van Gool, Radu Timofte

The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image priors into the prediction.

Burst Image Super-Resolution Denoising +2

Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution

1 code implementation ICCV 2021 Jingyun Liang, Guolei Sun, Kai Zhang, Luc van Gool, Radu Timofte

Extensive experiments on synthetic and real images show that the proposed MANet not only performs favorably for both spatially variant and invariant kernel estimation, but also leads to state-of-the-art blind SR performance when combined with non-blind SR methods.

Image Super-Resolution

Deep Homography for Efficient Stereo Image Compression

1 code implementation CVPR 2021 Xin Deng, Wenzhe Yang, Ren Yang, Mai Xu, Enpeng Liu, Qianhan Feng, Radu Timofte

To fully explore the mutual information across two stereo images, we use a deep regression model to estimate the homography matrix, i. e., H matrix.

Image Compression

Generative Flows with Invertible Attentions

no code implementations CVPR 2022 Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Radu Timofte, Luc van Gool

The key idea is to exploit a masked scheme of these two attentions to learn long-range data dependencies in the context of generative flows.

Image Generation

NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results

1 code implementation2 Jun 2021 Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Aleš Leonardis, Radu Timofte

This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021.

HDR Reconstruction Image Restoration

Fourier Space Losses for Efficient Perceptual Image Super-Resolution

no code implementations ICCV 2021 Dario Fuoli, Luc van Gool, Radu Timofte

As large models are often not practical in real-world applications, we investigate and propose novel loss functions, to enable SR with high perceptual quality from much more efficient models.

Image Super-Resolution

Fast and Accurate Camera Scene Detection on Smartphones

no code implementations17 May 2021 Angeline Pouget, Sidharth Ramesh, Maximilian Giang, Ramithan Chandrapalan, Toni Tanner, Moritz Prussing, Radu Timofte, Andrey Ignatov

AI-powered automatic camera scene detection mode is nowadays available in nearly any modern smartphone, though the problem of accurate scene prediction has not yet been addressed by the research community.

NTIRE 2021 Challenge on Video Super-Resolution

no code implementations30 Apr 2021 Sanghyun Son, Suyoung Lee, Seungjun Nah, Radu Timofte, Kyoung Mu Lee

Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart.

Video Super-Resolution

NTIRE 2021 Challenge on Image Deblurring

no code implementations30 Apr 2021 Seungjun Nah, Sanghyun Son, Suyoung Lee, Radu Timofte, Kyoung Mu Lee

In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions.

Deblurring Image Deblurring

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study

2 code implementations21 Apr 2021 Ren Yang, Radu Timofte

In our study, we analyze the proposed methods of the challenge and several methods in previous works on the proposed LDV dataset.

Video Enhancement

Towards Efficient Graph Convolutional Networks for Point Cloud Handling

no code implementations ICCV 2021 Yawei Li, He Chen, Zhaopeng Cui, Radu Timofte, Marc Pollefeys, Gregory Chirikjian, Luc van Gool

In this paper, we aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds.

Computational Efficiency

LocalViT: Bringing Locality to Vision Transformers

2 code implementations12 Apr 2021 Yawei Li, Kai Zhang, JieZhang Cao, Radu Timofte, Luc van Gool

The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and all proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to 4 vision transformers, which shows the generalization of the locality concept.

Image Classification

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution

3 code implementations ICCV 2021 Kai Zhang, Jingyun Liang, Luc van Gool, Radu Timofte

It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images.

Image Super-Resolution Video Super-Resolution

Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression

no code implementations9 Feb 2021 A. Murat Tekalp, Michele Covell, Radu Timofte, Chao Dong

Recent works have shown that learned models can achieve significant performance gains, especially in terms of perceptual quality measures, over traditional methods.

Image Restoration Video Restoration

Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution

no code implementations17 Jan 2021 Yan Wu, Zhiwu Huang, Suryansh Kumar, Rhea Sanjay Sukthanker, Radu Timofte, Luc van Gool

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 computationally efficient model.

Image Super-Resolution Neural Architecture Search

Local Memory Attention for Fast Video Semantic Segmentation

1 code implementation5 Jan 2021 Matthieu Paul, Martin Danelljan, Luc van Gool, Radu Timofte

Our approach aggregates a rich representation of the semantic information in past frames into a memory module.

Segmentation Semantic Segmentation +1

The Card Shuffling Hypotheses: Building a Time and Memory Efficient Graph Convolutional Network

no code implementations1 Jan 2021 Yawei Li, He Chen, Zhaopeng Cui, Radu Timofte, Marc Pollefeys, Gregory Chirikjian, Luc van Gool

State-of-the-art GCNs adopt $K$-nearest neighbor (KNN) searches for local feature aggregation and feature extraction operations from layer to layer.

3D Classification Point Cloud Classification +2

An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement

no code implementations24 Dec 2020 Dario Fuoli, Zhiwu Huang, Danda Pani Paudel, Luc van Gool, Radu Timofte

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 spatio-temporal domain.

Video Enhancement