Search Results for author: Radu Timofte

Found 139 papers, 74 papers with code

Perceptual Learned Video Compression with Recurrent Conditional GAN

1 code implementation7 Sep 2021 Ren Yang, Luc van Gool, Radu Timofte

The user study further validates the outstanding perceptual performance of PLVC in comparison with the latest learned video compression approaches and the official HEVC test model (HM 16. 20).

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.


SwinIR: Image Restoration Using Swin Transformer

1 code implementation23 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 Image Denoising +4

Deep Reparametrization of Multi-Frame Super-Resolution and Denoising

no code implementations18 Aug 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.

Denoising Image Restoration +1

Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling

1 code implementation11 Aug 2021 Jingyun Liang, Andreas Lugmayr, Kai Zhang, Martin Danelljan, Luc van Gool, Radu Timofte

More specifically, HCFlow learns a bijective mapping between HR and LR image pairs by modelling the distribution of the LR image and the rest high-frequency component simultaneously.

Image Super-Resolution

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

1 code implementation11 Aug 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.

Affine Transformation 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 implementations7 Jun 2021 Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Radu Timofte, Luc van Gool

Flow-based generative models have shown excellent ability to explicitly learn the probability density function of data via a sequence of invertible transformations.

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 implementations1 Jun 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.


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

1 code implementation21 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 implementations12 Apr 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.

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

1 code implementation25 Mar 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

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

no code implementations5 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.

Semantic Segmentation Video Semantic Segmentation

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 +1

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

Zero-Pair Image to Image Translation using Domain Conditional Normalization

1 code implementation11 Nov 2020 Samarth Shukla, Andrés Romero, Luc van Gool, Radu Timofte

In this paper, we propose an approach based on domain conditional normalization (DCN) for zero-pair image-to-image translation, i. e., translating between two domains which have no paired training data available but each have paired training data with a third domain.

Image-to-Image Translation

A Weakly Supervised Convolutional Network for Change Segmentation and Classification

1 code implementation6 Nov 2020 Philipp Andermatt, Radu Timofte

The core part of our model, the Change Segmentation and Classification (CSC) module, learns an accurate change mask at a hidden layer by using a custom Remapping Block and then segmenting the current input image with the change mask.

Classification General Classification

Self-Supervised Shadow Removal

no code implementations22 Oct 2020 Florin-Alexandru Vasluianu, Andres Romero, Luc van Gool, Radu Timofte

Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photo-realistic restoration of the image contents.

Image Shadow Removal Self-Supervised Learning +1

SMILE: Semantically-guided Multi-attribute Image and Layout Editing

1 code implementation5 Oct 2020 Andrés Romero, Luc van Gool, Radu Timofte

Additionally, our method is capable of adding, removing or changing either fine-grained or coarse attributes by using an image as a reference or by exploring the style distribution space, and it can be easily extended to head-swapping and face-reenactment applications without being trained on videos.

Face Reenactment Image Manipulation

AIM 2020 Challenge on Image Extreme Inpainting

2 code implementations2 Oct 2020 Evangelos Ntavelis, Andrés Romero, Siavash Bigdeli, Radu Timofte

This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting.

Image Inpainting Semantic Segmentation

Few-Shot Classification By Few-Iteration Meta-Learning

1 code implementation1 Oct 2020 Ardhendu Shekhar Tripathi, Martin Danelljan, Luc van Gool, Radu Timofte

By employing an efficient initialization module and a Steepest Descent based optimization algorithm, our base learner predicts a powerful classifier within only a few iterations.

Classification General Classification +2

MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search

no code implementations29 Sep 2020 Cristian Cioflan, Radu Timofte

Neural Architecture Search (NAS) has proved effective in offering outperforming alternatives to handcrafted neural networks.

Image Classification Neural Architecture Search

AIM 2020 Challenge on Video Temporal Super-Resolution

no code implementations28 Sep 2020 Sanghyun Son, Jaerin Lee, Seungjun Nah, Radu Timofte, Kyoung Mu Lee

Videos in the real-world contain various dynamics and motions that may look unnaturally discontinuous in time when the recordedframe rate is low.


Plug-and-Play Image Restoration with Deep Denoiser Prior

3 code implementations31 Aug 2020 Kai Zhang, Yawei Li, WangMeng Zuo, Lei Zhang, Luc van Gool, Radu Timofte

Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems.

Deblurring Demosaicking +1

OpenDVC: An Open Source Implementation of the DVC Video Compression Method

2 code implementations29 Jun 2020 Ren Yang, Luc van Gool, Radu Timofte

At the time of writing this report, several learned video compression methods are superior to DVC, but currently none of them provides open source codes.


The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures

1 code implementation CVPR 2021 Yawei Li, Wen Li, Martin Danelljan, Kai Zhang, Shuhang Gu, Luc van Gool, Radu Timofte

Based on that, we articulate the heterogeneity hypothesis: with the same training protocol, there exists a layer-wise differentiated network architecture (LW-DNA) that can outperform the original network with regular channel configurations but with a lower level of model complexity.

Image Classification Image Restoration +1

SRFlow: Learning the Super-Resolution Space with Normalizing Flow

3 code implementations ECCV 2020 Andreas Lugmayr, Martin Danelljan, Luc van Gool, Radu Timofte

SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.

Image Manipulation Super-Resolution

Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model

3 code implementations24 Jun 2020 Ren Yang, Fabian Mentzer, Luc van Gool, Radu Timofte

The experiments show that our approach achieves the state-of-the-art learned video compression performance in terms of both PSNR and MS-SSIM.


Rendering Natural Camera Bokeh Effect with Deep Learning

1 code implementation10 Jun 2020 Andrey Ignatov, Jagruti Patel, Radu Timofte

Bokeh is an important artistic effect used to highlight the main object of interest on the photo by blurring all out-of-focus areas.

Flexible Example-based Image Enhancement with Task Adaptive Global Feature Self-Guided Network

no code implementations13 May 2020 Dario Kneubuehler, Shuhang Gu, Luc van Gool, Radu Timofte

We propose the first practical multitask image enhancement network, that is able to learn one-to-many and many-to-one image mappings.

Image Enhancement

Learning Context-Based Non-local Entropy Modeling for Image Compression

no code implementations10 May 2020 Mu Li, Kai Zhang, WangMeng Zuo, Radu Timofte, David Zhang

To address this issue, we propose a non-local operation for context modeling by employing the global similarity within the context.

Image Compression

NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images

no code implementations7 May 2020 Codruta O. Ancuti, Cosmin Ancuti, Radu Timofte

The non-homogeneous haze has been introduced in the scene using a professional haze generator that imitates the real conditions of hazy scenes.

Image Dehazing Single Image Dehazing

NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image

no code implementations7 May 2020 Boaz Arad, Radu Timofte, Ohad Ben-Shahar, Yi-Tun Lin, Graham Finlayson, Shai Givati, others

This paper reviews the second challenge on spectral reconstruction from RGB images, i. e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image.

How to Train Your Energy-Based Model for Regression

1 code implementation4 May 2020 Fredrik K. Gustafsson, Martin Danelljan, Radu Timofte, Thomas B. Schön

While they are commonly employed for generative image modeling, recent work has applied EBMs also for regression tasks, achieving state-of-the-art performance on object detection and visual tracking.

Object Detection Visual Object Tracking +1

AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results

no code implementations4 May 2020 Seungjun Nah, Sanghyun Son, Radu Timofte, Kyoung Mu Lee

Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low.


NTIRE 2020 Challenge on Image and Video Deblurring

no code implementations4 May 2020 Seungjun Nah, Sanghyun Son, Radu Timofte, Kyoung Mu Lee

This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring.


Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning

no code implementations14 Apr 2020 Kangning Liu, Shuhang Gu, Andres Romero, Radu Timofte

Existing unsupervised video-to-video translation methods fail to produce translated videos which are frame-wise realistic, semantic information preserving and video-level consistent.

Self-Supervised Learning

SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects

1 code implementation ECCV 2020 Evangelos Ntavelis, Andrés Romero, Iason Kastanis, Luc van Gool, Radu Timofte

In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input, the SESAME discriminator is composed of two input streams that independently process the image and its semantics, using the latter to manipulate the results of the former.

 Ranked #1 on Image-to-Image Translation on Cityscapes Labels-to-Photo (Per-pixel Accuracy metric)

Image Manipulation Image-to-Image Translation

DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution

2 code implementations9 Apr 2020 Marcel C. Bühler, Andrés Romero, Radu Timofte

To the best of our knowledge, DeepSEE is the first method to leverage semantic maps for explorative super-resolution.

Face Hallucination Super-Resolution

DHP: Differentiable Meta Pruning via HyperNetworks

2 code implementations ECCV 2020 Yawei Li, Shuhang Gu, Kai Zhang, Luc van Gool, Radu Timofte

Passing the sparsified latent vectors through the hypernetworks, the corresponding slices of the generated weight parameters can be removed, achieving the effect of network pruning.

Denoising Image Classification +3

Probabilistic Regression for Visual Tracking

1 code implementation CVPR 2020 Martin Danelljan, Luc van Gool, Radu Timofte

In this work, we therefore propose a probabilistic regression formulation and apply it to tracking.

Visual Tracking

Learning What to Learn for Video Object Segmentation

2 code implementations ECCV 2020 Goutam Bhat, Felix Järemo Lawin, Martin Danelljan, Andreas Robinson, Michael Felsberg, Luc van Gool, Radu Timofte

This allows us to achieve a rich internal representation of the target in the current frame, significantly increasing the segmentation accuracy of our approach.

Few-Shot Learning One-shot visual object segmentation +3

Know Your Surroundings: Exploiting Scene Information for Object Tracking

1 code implementation ECCV 2020 Goutam Bhat, Martin Danelljan, Luc van Gool, Radu Timofte

Such approaches are however prone to fail in case of e. g. fast appearance changes or presence of distractor objects, where a target appearance model alone is insufficient for robust tracking.

Object Tracking

Deep Unfolding Network for Image Super-Resolution

1 code implementation CVPR 2020 Kai Zhang, Luc van Gool, Radu Timofte

As a result, the proposed network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning-based methods.

Image Super-Resolution

Self-Supervised 2D Image to 3D Shape Translation with Disentangled Representations

no code implementations22 Mar 2020 Berk Kaya, Radu Timofte

In this paper, we propose SIST, a Self-supervised Image to Shape Translation framework that fulfills three tasks: (i) reconstructing the 3D shape from a single image; (ii) learning disentangled representations for shape, appearance and viewpoint; and (iii) generating a realistic RGB image from these independent factors.

Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression

2 code implementations CVPR 2020 Yawei Li, Shuhang Gu, Christoph Mayer, Luc van Gool, Radu Timofte

In this paper, we analyze two popular network compression techniques, i. e. filter pruning and low-rank decomposition, in a unified sense.

Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement

3 code implementations CVPR 2020 Ren Yang, Fabian Mentzer, Luc van Gool, Radu Timofte

In our HLVC approach, the hierarchical quality benefits the coding efficiency, since the high quality information facilitates the compression and enhancement of low quality frames at encoder and decoder sides, respectively.

Image Compression MS-SSIM +2

Replacing Mobile Camera ISP with a Single Deep Learning Model

2 code implementations13 Feb 2020 Andrey Ignatov, Luc van Gool, Radu Timofte

The model is trained to convert RAW Bayer data obtained directly from mobile camera sensor into photos captured with a professional high-end DSLR camera, making the solution independent of any particular mobile ISP implementation.

Demosaicking Denoising

Efficient Video Semantic Segmentation with Labels Propagation and Refinement

no code implementations26 Dec 2019 Matthieu Paul, Christoph Mayer, Luc van Gool, Radu Timofte

(ii) On the GPU, two Convolutional Neural Networks: A main segmentation network that is used to predict dense semantic labels from scratch, and a Refiner that is designed to improve predictions from previous frames with the help of a fast Inconsistencies Attention Module (IAM).

Optical Flow Estimation Real-Time Semantic Segmentation +2

GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences

4 code implementations CVPR 2020 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.

Dense Pixel Correspondence Estimation Geometric Matching +1

Frequency Separation for Real-World Super-Resolution

2 code implementations18 Nov 2019 Manuel Fritsche, Shuhang Gu, Radu Timofte

Furthermore, we propose to separate the low and high image frequencies and treat them differently during training.

Image Super-Resolution

AIM 2019 Challenge on Image Demoireing: Dataset and Study

no code implementations6 Nov 2019 Shanxin Yuan, Radu Timofte, Gregory Slabaugh, Ales Leonardis

In addition to describing the dataset and its creation, this paper also reviews the challenge tracks, competition, and results, the latter summarizing the current state-of-the-art on this dataset.

Image Manipulation

Divide-and-Conquer Adversarial Learning for High-Resolution Image and Video Enhancement

no code implementations23 Oct 2019 Zhiwu Huang, Danda Pani Paudel, Guanju Li, Jiqing Wu, Radu Timofte, Luc van Gool

This paper introduces a divide-and-conquer inspired adversarial learning (DACAL) approach for photo enhancement.

Video Enhancement

AI Benchmark: All About Deep Learning on Smartphones in 2019

no code implementations15 Oct 2019 Andrey Ignatov, Radu Timofte, Andrei Kulik, Seungsoo Yang, Ke Wang, Felix Baum, Max Wu, Lirong Xu, Luc van Gool

The performance of mobile AI accelerators has been evolving rapidly in the past two years, nearly doubling with each new generation of SoCs.

Unsupervised Learning for Real-World Super-Resolution

no code implementations20 Sep 2019 Andreas Lugmayr, Martin Danelljan, Radu Timofte

Instead of directly addressing this problem, most works employ the popular bicubic downsampling strategy to artificially generate a corresponding low resolution image.

Image Super-Resolution

Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation

1 code implementation18 Sep 2019 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.

Image-to-Image Translation Semantic Segmentation

Efficient Video Super-Resolution through Recurrent Latent Space Propagation

1 code implementation17 Sep 2019 Dario Fuoli, Shuhang Gu, Radu Timofte

However, as the motion estimation problem is a highly challenging problem, inaccurate motion compensation may affect the performance of VSR algorithms.

Video Super-Resolution Image and Video Processing

Learning Filter Basis for Convolutional Neural Network Compression

3 code implementations ICCV 2019 Yawei Li, Shuhang Gu, Luc van Gool, Radu Timofte

Convolutional neural networks (CNNs) based solutions have achieved state-of-the-art performances for many computer vision tasks, including classification and super-resolution of images.

General Classification Image Classification +2

Exemplar Guided Face Image Super-Resolution without Facial Landmarks

1 code implementation17 Jun 2019 Berk Dogan, Shuhang Gu, Radu Timofte

Nowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images.

Image Super-Resolution

3D Appearance Super-Resolution with Deep Learning

1 code implementation CVPR 2019 Yawei Li, Vagia Tsiminaki, Radu Timofte, Marc Pollefeys, Luc van Gool

Experimental results demonstrate that our proposed networks successfully incorporate the 3D geometric information and super-resolve the texture maps.


Learning Discriminative Model Prediction for Tracking

2 code implementations ICCV 2019 Goutam Bhat, Martin Danelljan, Luc van Gool, Radu Timofte

The current strive towards end-to-end trainable computer vision systems imposes major challenges for the task of visual tracking.

Visual Object Tracking Visual Tracking

Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images

1 code implementation5 Apr 2019 Codruta O. Ancuti, Cosmin Ancuti, Mateu Sbert, Radu Timofte

Characterized by dense and homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes.

Image Dehazing Single Image Dehazing

PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study

no code implementations1 Apr 2019 Mehrdad Shoeiby, Antonio Robles-Kelly, Ran Wei, Radu Timofte

This paper introduces a newly collected and novel dataset (StereoMSI) for example-based single and colour-guided spectral image super-resolution.

Image Super-Resolution

Fast Perceptual Image Enhancement

1 code implementation31 Dec 2018 Etienne de Stoutz, Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Luc van Gool

We extend upon the results of Ignatov et al., where they are able to translate images from compact mobile cameras into images with comparable quality to high-resolution photos taken by DSLR cameras.

Image Enhancement

SMIT: Stochastic Multi-Label Image-to-Image Translation

1 code implementation10 Dec 2018 Andrés Romero, Pablo Arbeláez, Luc van Gool, Radu Timofte

This problem is highly challenging due to three main reasons: (i) unpaired datasets, (ii) multiple attributes, and (iii) the multimodality (e. g., style) associated with the translation.

Image-to-Image Translation

Towards Spectral Estimation from a Single RGB Image in the Wild

no code implementations3 Dec 2018 Berk Kaya, Yigit Baran Can, Radu Timofte

In contrast to the current literature, we address the problem of estimating the spectrum from a single common trichromatic RGB image obtained under unconstrained settings (e. g. unknown camera parameters, unknown scene radiance, unknown scene contents).

Spectral Estimation From A Single Rgb Image

Practical Full Resolution Learned Lossless Image Compression

2 code implementations CVPR 2019 Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc van Gool

We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000.

Image Compression

AI Benchmark: Running Deep Neural Networks on Android Smartphones

1 code implementation2 Oct 2018 Andrey Ignatov, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim Hartley, Luc van Gool

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 ago.

Night-to-Day Image Translation for Retrieval-based Localization

1 code implementation26 Sep 2018 Asha Anoosheh, Torsten Sattler, Radu Timofte, Marc Pollefeys, Luc van Gool

We then compare the daytime and translated night images to obtain a pose estimate for the night image using the known 6-DOF position of the closest day image.

Image Retrieval Style Generalization +1

The 2018 PIRM Challenge on Perceptual Image Super-resolution

5 code implementations20 Sep 2018 Yochai Blau, Roey Mechrez, Radu Timofte, Tomer Michaeli, Lihi Zelnik-Manor

This paper reports on the 2018 PIRM challenge on perceptual super-resolution (SR), held in conjunction with the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018.

Image Restoration Image Super-Resolution

Adversarial Sampling for Active Learning

no code implementations ICLR 2019 Christoph Mayer, Radu Timofte

This paper proposes asal, a new GAN based active learning method that generates high entropy samples.

Active Learning General Classification +1

Multi-bin Trainable Linear Unit for Fast Image Restoration Networks

no code implementations30 Jul 2018 Shuhang Gu, Radu Timofte, Luc van Gool

Tremendous advances in image restoration tasks such as denoising and super-resolution have been achieved using neural networks.

Image Denoising Image Restoration +1

O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images

1 code implementation13 Apr 2018 Codruta O. Ancuti, Cosmin Ancuti, Radu Timofte, Christophe De Vleeschouwer

Haze removal or dehazing is a challenging ill-posed problem that has drawn a significant attention in the last few years.


I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images

2 code implementations13 Apr 2018 Codruta O. Ancuti, Cosmin Ancuti, Radu Timofte, Christophe De Vleeschouwer

This represents an important advantage of the I-HAZE dataset that allows us to objectively compare the existing image dehazing techniques using traditional image quality metrics such as PSNR and SSIM.

Image Dehazing SSIM

An efficient CNN for spectral reconstruction from RGB images

1 code implementation12 Apr 2018 Yigit Baran Can, Radu Timofte

Recently, the example-based single image spectral reconstruction from RGB images task, aka, spectral super-resolution was approached by means of deep learning by Galliani et al.

Image Super-Resolution

Towards Image Understanding from Deep Compression without Decoding

1 code implementation ICLR 2018 Robert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc van Gool

Motivated by recent work on deep neural network (DNN)-based image compression methods showing potential improvements in image quality, savings in storage, and bandwidth reduction, we propose to perform image understanding tasks such as classification and segmentation directly on the compressed representations produced by these compression methods.

Classification General Classification +1

Conditional Probability Models for Deep Image Compression

1 code implementation CVPR 2018 Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc van Gool

During training, the auto-encoder makes use of the context model to estimate the entropy of its representation, and the context model is concurrently updated to learn the dependencies between the symbols in the latent representation.

Image Compression MS-SSIM +2

ComboGAN: Unrestrained Scalability for Image Domain Translation

1 code implementation19 Dec 2017 Asha Anoosheh, Eirikur Agustsson, Radu Timofte, Luc Van Gool

This year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al.

Image-to-Image Translation

Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks

no code implementations CVPR 2018 Alexander Sage, Eirikur Agustsson, Radu Timofte, Luc van Gool

We propose the use of synthetic labels obtained through clustering to disentangle and stabilize GAN training.

Optimal transport maps for distribution preserving operations on latent spaces of Generative Models

no code implementations ICLR 2018 Eirikur Agustsson, Alexander Sage, Radu Timofte, Luc van Gool

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 as uniform or Gaussian.

Anchored Regression Networks Applied to Age Estimation and Super Resolution

no code implementations ICCV 2017 Eirikur Agustsson, Radu Timofte, Luc van Gool

We propose the Anchored Regression Network (ARN), a nonlinear regression network which can be seamlessly integrated into various networks or can be used stand-alone when the features have already been fixed.

Age Estimation Image Super-Resolution

WESPE: Weakly Supervised Photo Enhancer for Digital Cameras

3 code implementations4 Sep 2017 Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, Luc van Gool

Low-end and compact mobile cameras demonstrate limited photo quality mainly due to space, hardware and budget constraints.

DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks

3 code implementations ICCV 2017 Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, Luc van Gool

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 to achieve the quality results of DSLR cameras.

On the Relation between Color Image Denoising and Classification

no code implementations5 Apr 2017 Jiqing Wu, Radu Timofte, Zhiwu Huang, Luc van Gool

Inspired by classification models, we propose a novel deep learning architecture for color (multichannel) image denoising and report on thousands of images from ImageNet dataset as well as commonly used imagery.

Classification Color Image Denoising +2

Single Image Super Resolution - When Model Adaptation Matters

no code implementations31 Mar 2017 Yudong Liang, Radu Timofte, Jinjun Wang, Yihong Gong, Nanning Zheng

The internal contents of the low resolution input image is neglected with deep modeling despite the earlier works showing the power of using such internal priors.

Image Super-Resolution

Failure Detection for Facial Landmark Detectors

no code implementations23 Aug 2016 Andreas Steger, Radu Timofte, Luc van Gool

Most face applications depend heavily on the accuracy of the face and facial landmarks detectors employed.

Facial Landmark Detection

Generic 3D Convolutional Fusion for image restoration

no code implementations26 Jul 2016 Jiqing Wu, Radu Timofte, Luc van Gool

Unlike other methods adapted to different tasks, our method uses the exact same convolutional network architecture to address both image denois- ing and single image super-resolution.

Image Denoising Image Restoration +1

k2-means for fast and accurate large scale clustering

no code implementations30 May 2016 Eirikur Agustsson, Radu Timofte, Luc van Gool

k^2-means builds upon the standard k-means (Lloyd's algorithm) and combines a new strategy to accelerate the convergence with a new low time complexity divisive initialization.

Fast Optical Flow using Dense Inverse Search

no code implementations11 Mar 2016 Till Kroeger, Radu Timofte, Dengxin Dai, Luc van Gool

Most recent works in optical flow extraction focus on the accuracy and neglect the time complexity.

Action Detection Activity Detection +1

Seven ways to improve example-based single image super resolution

no code implementations CVPR 2016 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 dictionaries with efficient search structures, 3) cascading, 4) image self-similarities, 5) back projection refinement, 6) enhanced prediction by consistency check, and 7) context reasoning.

Image Super-Resolution