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Sabine Süsstrunk - IEEE Xplore Author Profile

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Author details

Sabine Süsstrunk

Also published under: S. Susstrunk, Sabine Susstrunk, S. Süsstrunk

Publications

87

Citations

13,639

Publications by Year

20002023

Co-Authors:

Radhakrishna AchantaDavid AlleyssonJonas Toft ArnfredNikolaos ArvanitopoulosAngelica I. Aviles-Rivero

Show All Co-Authors (169)

Sabine Süsstrunk

Also published under: S. Susstrunk, Sabine Susstrunk, S. Süsstrunk

Affiliation

School of Computer and Communication Sciences, EPFL

Publication Topics

Convolutional Neural Network,
Attention Mechanism,
Semantic Segmentation,
Depth Estimation,
Domain Adaptation,
Neural Network,
Pre-trained Network,
Additive Noise,
Adversarial Attacks,
Adversarial Examples,
Data Augmentation,
Decoding Stage

Biography

Sabine Süsstrunk (M’03–SM’09–F’17) leads the Images and Visual Representation Lab (IVRL), EPFL, Switzerland. She has published over 150 scientific papers, of which seven have received the best paper/demos awards. She holds ten patents. Her research areas are in computational photography, color computer vision and color image processing, image quality, and computational aesthetics. She is a fellow of the IS&T. She was a recipient of the IS&T/SPIE 2013 Electronic Imaging Scientist of the Year Award and the IS&T’s 2018 Raymond C. Bowman Award.(Based on document published on 1 July 2019).

Publications

87

Citations

13,639

Publications by Year

20002023

Co-Authors:

Radhakrishna Achanta
David Alleysson
Jonas Toft Arnfred
Nikolaos Arvanitopoulos
Angelica I. Aviles-Rivero

Show All Co-Authors (169)

Author's Published Works

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Showing 1-25 of 87 results

Conferences (69)

Journals (18)

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Sabine Süsstrunk(71)
Majed El Helou(9)
Radhakrishna Achanta(9)
Mathieu Salzmann(9)
Sabine Susstrunk(8)
School of Computer and Communication Sciences, EPFL, Switzerland(16)
School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland(8)
School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland(6)
School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland(3)
Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA(3)
IEEE Transactions on Image Processing(8)
IEEE Transactions on Pattern Analysis and Machine Intelligence(5)
2009 16th IEEE International Conference on Image Processing (ICIP)(4)
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(4)
2017 IEEE International Conference on Image Processing (ICIP)(4)
IEEE(87)
Media(9)
Vancouver, BC, Canada(6)
Beijing, China(4)
Cairo, Egypt(4)
Honolulu, HI, USA(4)
New Orleans, LA, USA(4)
Color Images(23)
Convolutional Neural Network(20)
Input Image(14)
Neural Network(13)
Near-infrared Imaging(12)

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NEMTO: Neural Environment Matting for Novel View and Relighting Synthesis of Transparent Objects

Dongqing Wang;Tong Zhang;Sabine Süsstrunk

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (7)

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We propose NEMTO, the first end-to-end neural rendering pipeline to model 3D transparent objects with complex geometry and unknown indices of refraction. Commonly used appearance modeling such as the Disney BSDF model cannot accurately address this challenging problem due to the complex light paths bending through refractions and the strong dependency of surface appearance on illumination. With 2D...Show More

NEMTO: Neural Environment Matting for Novel View and Relighting Synthesis of Transparent Objects

Dongqing Wang;Tong Zhang;Sabine Süsstrunk

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Vision Transformer Adapters for Generalizable Multitask Learning

Deblina Bhattacharjee;Sabine Süsstrunk;Mathieu Salzmann

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (3)

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We introduce the first multitasking vision transformer adapters that learn generalizable task affinities which can be applied to novel tasks and domains. Integrated into an off-the-shelf vision transformer backbone, our adapters can simultaneously solve multiple dense vision tasks in a parameter-efficient manner, unlike existing multitasking transformers that are parametrically expensive. In contr...Show More

Vision Transformer Adapters for Generalizable Multitask Learning

Deblina Bhattacharjee;Sabine Süsstrunk;Mathieu Salzmann

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Fast Adversarial Training With Adaptive Step Size

Zhichao Huang;Yanbo Fan;Chen Liu;Weizhong Zhang;Yong Zhang;Mathieu Salzmann;Sabine Süsstrunk;Jue Wang

IEEE Transactions on Image Processing

Year: 2023 | Volume: 32 | Journal Article |

Cited by: Papers (10)

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While adversarial training and its variants have shown to be the most effective algorithms to defend against adversarial attacks, their extremely slow training process makes it hard to scale to large datasets like ImageNet. The key idea of recent works to accelerate adversarial training is to substitute multi-step attacks (e.g., PGD) with single-step attacks (e.g., FGSM). However, these single-ste...Show More

Fast Adversarial Training With Adaptive Step Size

Zhichao Huang;Yanbo Fan;Chen Liu;Weizhong Zhang;Yong Zhang;Mathieu Salzmann;Sabine Süsstrunk;Jue Wang

IEEE Transactions on Image Processing

Year: 2023 | Volume: 32 | Journal Article |

Spatiotemporal Self-Supervised Learning for Point Clouds in the Wild

Yanhao Wu;Tong Zhang;Wei Ke;Sabine Susstrunk;Mathieu Salzmann

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (14)

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Self-supervised learning (SSL) has the potential to benefit many applications, particularly those where manually annotating data is cumbersome. One such situation is the semantic segmentation of point clouds. In this context, existing methods employ contrastive learning strategies and define positive pairs by performing various augmentation of point clusters in a single frame. As such, these metho...Show More

Spatiotemporal Self-Supervised Learning for Point Clouds in the Wild

Yanhao Wu;Tong Zhang;Wei Ke;Sabine Susstrunk;Mathieu Salzmann

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction

Yufan Ren;Fangjinhua Wang;Tong Zhang;Marc Pollefeys;Sabine Süsstrunk

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (19)

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The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction. However, most existing neural implicit reconstruction methods optimize perscene parameters and therefore lack generalizability to new scenes. We introduce VolRecon, a novel generalizable implicit reconstruction method with Signed Ray Distance Function (...Show More

VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction

Yufan Ren;Fangjinhua Wang;Tong Zhang;Marc Pollefeys;Sabine Süsstrunk

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

TempSAL - Uncovering Temporal Information for Deep Saliency Prediction

Bahar Aydemir;Ludo Hoffstetter;Tong Zhang;Mathieu Salzmann;Sabine Süsstrunk

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (7)

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Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information such as scene context, semantic relationships, gaze direction, and object dissimilarity. However, none of these models consider the temporal nature of gaze shifts during image observation. We introduce a novel saliency prediction model that learns to output saliency maps in...Show More

TempSAL - Uncovering Temporal Information for Deep Saliency Prediction

Bahar Aydemir;Ludo Hoffstetter;Tong Zhang;Mathieu Salzmann;Sabine Süsstrunk

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

DyNCA: Real-Time Dynamic Texture Synthesis Using Neural Cellular Automata

Ehsan Pajouheshgar;Yitao Xu;Tong Zhang;Sabine Süsstrunk

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (5)

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Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos. However, they require a slow iterative optimization process to synthesize a single fixed-size short video, and they do not offer any post-training control over the synthesis process. We propose Dynamic Neural Cellular Automata (DyNCA), a framework for real-time and controllable dynamic texture synthesis. Our method is...Show More

DyNCA: Real-Time Dynamic Texture Synthesis Using Neural Cellular Automata

Ehsan Pajouheshgar;Yitao Xu;Tong Zhang;Sabine Süsstrunk

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Dense Multitask Learning to Reconfigure Comics

Deblina Bhattacharjee;Sabine Süsstrunk;Mathieu Salzmann

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2023 | Conference Paper |

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In this paper, we develop a MultiTask Learning (MTL) model to achieve dense predictions for comics panels to, in turn, facilitate the transfer of comics from one publication channel to another by assisting authors in the task of reconfiguring their narratives. Our MTL method can successfully identify the semantic units as well as the embedded notion of 3D in comics panels. This is a significantly ...Show More

Dense Multitask Learning to Reconfigure Comics

Deblina Bhattacharjee;Sabine Süsstrunk;Mathieu Salzmann

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2023 | Conference Paper |

Training Provably Robust Models by Polyhedral Envelope Regularization

Chen Liu;Mathieu Salzmann;Sabine Süsstrunk

IEEE Transactions on Neural Networks and Learning Systems

Year: 2023 | Volume: 34, Issue: 6 | Journal Article |

Cited by: Papers (5)

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Training certifiable neural networks enables us to obtain models with robustness guarantees against adversarial attacks. In this work, we introduce a framework to obtain a provable adversarial-free region in the neighborhood of the input data by a polyhedral envelope, which yields more fine-grained certified robustness than existing methods. We further introduce polyhedral envelope regularization ...Show More

Training Provably Robust Models by Polyhedral Envelope Regularization

Chen Liu;Mathieu Salzmann;Sabine Süsstrunk

IEEE Transactions on Neural Networks and Learning Systems

Year: 2023 | Volume: 34, Issue: 6 | Journal Article |

PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

Nicolas Bähler;Majed El Helou;Étienne Objois;Kaan Okumuş;Sabine Süsstrunk

IEEE Signal Processing Letters

Year: 2022 | Volume: 29 | Journal Article |

Cited by: Papers (5)

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Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. No method is currently available to exploit the noise-free information, which may help to achieve more accurate estimations. To fill this gap, we...Show More

PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

Nicolas Bähler;Majed El Helou;Étienne Objois;Kaan Okumuş;Sabine Süsstrunk

IEEE Signal Processing Letters

Year: 2022 | Volume: 29 | Journal Article |

Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy

Tong Zhang;Congpei Qiu;Wei Ke;Sabine Süsstrunk;Mathieu Salzmann

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (15)

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Self-supervised learning (SSL) methods aim to learn view-invariant representations by maximizing the similar-ity between the features extracted from different crops of the same image regardless of cropping size and content. In essence, this strategy ignores the fact that two crops may truly contain different image information, e.g., background and small objects, and thus tends to restrain the dive...Show More

Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy

Tong Zhang;Congpei Qiu;Wei Ke;Sabine Süsstrunk;Mathieu Salzmann

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

MuIT: An End-to-End Multitask Learning Transformer

Deblina Bhattacharjee;Tong Zhang;Sabine Süsstrunk;Mathieu Salzmann

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (36)

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We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edge detection. Based on the Swin transformer model, our framework encodes the input image into a shared representation and makes predictions for e...Show More

MuIT: An End-to-End Multitask Learning Transformer

Deblina Bhattacharjee;Tong Zhang;Sabine Süsstrunk;Mathieu Salzmann

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Optimizing Latent Space Directions for Gan-Based Local Image Editing

Ehsan Pajouheshgar;Tong Zhang;Sabine Süsstrunk

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2022 | Conference Paper |

Cited by: Papers (9)

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Generative Adversarial Network (GAN) based localized image editing can suffer from ambiguity between semantic at-tributes. We thus present a novel objective function to evaluate the locality of an image edit. By introducing the super-vision from a pre-trained segmentation network and optimizing the objective function, our framework, called Locally Effective Latent Space Direction (LELSD), is appli...Show More

Optimizing Latent Space Directions for Gan-Based Local Image Editing

Ehsan Pajouheshgar;Tong Zhang;Sabine Süsstrunk

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2022 | Conference Paper |

Natural-Looking Adversarial Examples from Freehand Sketches

Hak Gu Kim;Davide Nanni;Sabine Süsstrunk

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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Deep neural networks (DNNs) have achieved great success in image classification and recognition compared to previous methods. However, recent works have reported that DNNs are very vulnerable to adversarial examples that are intentionally generated to mislead the predictions of the DNNs. Here, we present a novel freehand sketch-based natural-looking adversarial example generator that we call Sketc...Show More

Natural-Looking Adversarial Examples from Freehand Sketches

Hak Gu Kim;Davide Nanni;Sabine Süsstrunk

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2022 | Conference Paper |

Estimating Image Depth in the Comics Domain

Deblina Bhattacharjee;Martin Everaert;Mathieu Salzmann;Sabine Süsstrunk

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

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Estimating the depth of comics images is challenging as such images a) are monocular; b) lack ground-truth depth annotations; c) differ across different artistic styles; d) are sparse and noisy. We thus, use an off-the-shelf unsupervised image to image translation method to translate the comics images to natural ones and then use an attention-guided monocular depth estimator to predict their depth...Show More

Estimating Image Depth in the Comics Domain

Deblina Bhattacharjee;Martin Everaert;Mathieu Salzmann;Sabine Süsstrunk

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2022 | Conference Paper |

BIGPrior: Toward Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration

Majed El Helou;Sabine Süsstrunk

IEEE Transactions on Image Processing

Year: 2022 | Volume: 31 | Journal Article |

Cited by: Papers (8)

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Classic image-restoration algorithms use a variety of priors, either implicitly or explicitly. Their priors are hand-designed and their corresponding weights are heuristically assigned. Hence, deep learning methods often produce superior image restoration quality. Deep networks are, however, capable of inducing strong and hardly predictable hallucinations. Networks implicitly learn to be jointly f...Show More

BIGPrior: Toward Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration

Majed El Helou;Sabine Süsstrunk

IEEE Transactions on Image Processing

Year: 2022 | Volume: 31 | Journal Article |

NTIRE 2021 Depth Guided Image Relighting Challenge

Majed El Helou;Ruofan Zhou;Sabine Süsstrunk;Radu Timofte;Maitreya Suin;A. N. Rajagopalan;Yuanzhi Wang;Tao Lu;Yanduo Zhang;Yuntao Wu;Hao-Hsiang Yang;Wei-Ting Chen;Sy-Yen Kuo;Hao-Lun Luo;Zhiguang Zhang;Zhipeng Luo;Jianye He;Zuo-Liang Zhu;Zhen Li;Jia-Xiong Qiu;Zeng-Sheng Kuang;Cheng-Ze Lu;Ming-Ming Cheng;Xiu-Li Shao;Chenghua Li;Bosong Ding;Wanli Qian;Fangya Li;Fu Li;Ruifeng Deng;Tianwei Lin;Songhua Liu;Xin Li;Dongliang He;Amirsaeed Yazdani;Tiantong Guo;Vishal Monga;Ntumba Elie Nsampi;Zhongyun Hu;Qing Wang;Sabari Nathan;Priya Kansal;Tongtong Zhao;Shanshan Zhao

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2021 | Conference Paper |

Cited by: Papers (19)

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Image relighting is attracting increasing interest due to its various applications. From a research perspective, im-age relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge.We ...Show More

NTIRE 2021 Depth Guided Image Relighting Challenge

Majed El Helou;Ruofan Zhou;Sabine Süsstrunk;Radu Timofte;Maitreya Suin;A. N. Rajagopalan;Yuanzhi Wang;Tao Lu;Yanduo Zhang;Yuntao Wu;Hao-Hsiang Yang;Wei-Ting Chen;Sy-Yen Kuo;Hao-Lun Luo;Zhiguang Zhang;Zhipeng Luo;Jianye He;Zuo-Liang Zhu;Zhen Li;Jia-Xiong Qiu;Zeng-Sheng Kuang;Cheng-Ze Lu;Ming-Ming Cheng;Xiu-Li Shao;Chenghua Li;Bosong Ding;Wanli Qian;Fangya Li;Fu Li;Ruifeng Deng;Tianwei Lin;Songhua Liu;Xin Li;Dongliang He;Amirsaeed Yazdani;Tiantong Guo;Vishal Monga;Ntumba Elie Nsampi;Zhongyun Hu;Qing Wang;Sabari Nathan;Priya Kansal;Tongtong Zhao;Shanshan Zhao

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2021 | Conference Paper |

Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion

Xiaoqi Ma;Xiaoyu Lin;Majed El Helou;Sabine Süsstrunk

2021 IEEE International Conference on Image Processing (ICIP)

Year: 2021 | Conference Paper |

Cited by: Papers (3)

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Following the performance breakthrough of denoising networks, improvements have come chiefly through novel architecture designs and increased depth. While novel denoising networks were designed for real images coming from different distributions, or for specific applications, comparatively small improvement was achieved on Gaussian denoising. The denoising solutions suffer from epistemic uncertain...Show More

Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion

Xiaoqi Ma;Xiaoyu Lin;Majed El Helou;Sabine Süsstrunk

2021 IEEE International Conference on Image Processing (ICIP)

Year: 2021 | Conference Paper |

Fidelity Estimation Improves Noisy-Image Classification With Pretrained Networks

Xiaoyu Lin;Deblina Bhattacharjee;Majed El Helou;Sabine Süsstrunk

IEEE Signal Processing Letters

Year: 2021 | Volume: 28 | Journal Article |

Cited by: Papers (6)

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Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are trained on clean images and are not robust when handling noisy ones, even if a restoration preprocessing step is applied. While novel methods a...Show More

Fidelity Estimation Improves Noisy-Image Classification With Pretrained Networks

Xiaoyu Lin;Deblina Bhattacharjee;Majed El Helou;Sabine Süsstrunk

IEEE Signal Processing Letters

Year: 2021 | Volume: 28 | Journal Article |

Editing in Style: Uncovering the Local Semantics of GANs

Edo Collins;Raja Bala;Bob Price;Sabine Süsstrunk

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Cited by: Papers (177) | Patents (1)

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While the quality of GAN image synthesis has improved tremendously in recent years, our ability to control and condition the output is still limited. Focusing on StyleGAN, we introduce a simple and effective method for making local, semantically-aware edits to a target output image. This is accomplished by borrowing elements from a source image, also a GAN output, via a novel manipulation of style...Show More

Editing in Style: Uncovering the Local Semantics of GANs

Edo Collins;Raja Bala;Bob Price;Sabine Süsstrunk

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Majed El Helou;Frederike Dumbgen;Sabine Süsstrunk

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2020 | Conference Paper |

Cited by: Papers (2)

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The large capacity of neural networks enables them to learn complex functions. To avoid overfitting, networks however require a lot of training data that can be expensive and time-consuming to collect. A common practical approach to attenuate overfitting is the use of network regularization techniques.We propose a novel regularization method that progressively penalizes the magnitude of activation...Show More

AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Majed El Helou;Frederike Dumbgen;Sabine Süsstrunk

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2020 | Conference Paper |

Divergence-Based Adaptive Extreme Video Completion

Majed El Helou;Ruofan Zhou;Frank Schmutz;Fabrice Guibert;Sabine Süsstrunk

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2020 | Conference Paper |

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Extreme image or video completion, where, for instance, we only retain 1% of pixels in random locations, allows for very cheap sampling in terms of the required pre-processing. The consequence is, however, a reconstruction that is challenging for humans and inpainting algorithms alike. We propose an extension of a state-of-the-art extreme image completion algorithm to extreme video completion. We ...Show More

Divergence-Based Adaptive Extreme Video Completion

Majed El Helou;Ruofan Zhou;Frank Schmutz;Fabrice Guibert;Sabine Süsstrunk

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2020 | Conference Paper |

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

Majed El Helou;Sabine Süsstrunk

IEEE Transactions on Image Processing

Year: 2020 | Volume: 29 | Journal Article |

Cited by: Papers (85)

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Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded blind and universal deep learning image denoiser for additive Gaussian noise removal. Our network is based on an optimal denoising soluti...Show More

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

Majed El Helou;Sabine Süsstrunk

IEEE Transactions on Image Processing

Year: 2020 | Volume: 29 | Journal Article |

Zero-Learning Fast Medical Image Fusion

Fayez Lahoud;Sabine Süsstrunk

2019 22th International Conference on Information Fusion (FUSION)

Year: 2019 | Conference Paper |

Cited by: Papers (41)

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Clinical applications, such as image-guided surgery and noninvasive diagnosis, rely heavily on multi-modal images. Medical image fusion plays a central role by integrating information from multiple sources into a single, more understandable output. We propose a real-time image fusion method using pre-trained neural networks to generate a single image containing features from multi-modal sources. T...Show More

Zero-Learning Fast Medical Image Fusion

Fayez Lahoud;Sabine Süsstrunk

2019 22th International Conference on Information Fusion (FUSION)

Year: 2019 | Conference Paper |

Kernel Modeling Super-Resolution on Real Low-Resolution Images

Ruofan Zhou;Sabine Süsstrunk

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2019 | Conference Paper |

Cited by: Papers (103)

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Deep convolutional neural networks (CNNs), trained on corresponding pairs of high- and low-resolution images, achieve state-of-the-art performance in single-image super-resolution and surpass previous signal-processing based approaches. However, their performance is limited when applied to real photographs. The reason lies in their training data: low-resolution (LR) images are obtained by bicubic ...Show More

Kernel Modeling Super-Resolution on Real Low-Resolution Images

Ruofan Zhou;Sabine Süsstrunk

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2019 | Conference Paper |

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