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Sabine Süsstrunk

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Publications associées (188)

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

Sabine Süsstrunk, Majed El Helou

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-gr ...

IEEE2020

Image Restoration using Plug-and-Play CNN MAP Denoisers

Sabine Süsstrunk, Siavash Arjomand Bigdeli, David Honzátko

Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a MAP d ...

SCITEPRESS2020

W2S: Microscopy Data with Joint Denoising and Super-Resolution for Widefield to SIM Mapping

Daniel Sage, Sabine Süsstrunk, Thierry Laroche, Arne Seitz, Ruofan Zhou, Majed El Helou

In fluorescence microscopy live-cell imaging, there is a critical trade-off between the signal-to-noise ratio and spatial resolution on one side, and the integrity of the biological sample on the other side. To obtain clean high-resolution (HR) images, one ...

2020

Volumetric Transformer Networks

Mathieu Salzmann, Sabine Süsstrunk, Seungryong Kim

Existing techniques to encode spatial invariance within deep convolutional neural networks (CNNs) apply the same warping field to all the feature channels. This does not account for the fact that the individual feature channels can represent different sema ...

2020

Divergence-Based Adaptive Extreme Video Completion

Sabine Süsstrunk, Ruofan Zhou, Fabrice Jean Guibert, Frank Schmutz, Majed El Helou

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 an ...

IEEE2020

AIM 2020: Scene Relighting and Illumination Estimation Challenge

Sabine Süsstrunk, Radu Timofte, Matthew Brown, Ruofan Zhou, Majed El Helou, Tongtong Zhao, Shanshan Zhao, Yu Zhu, Xin Huang, Yuzhong Liu

We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks. The f ...

2020

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

Sabine Süsstrunk, Frederike Dümbgen, Majed El Helou

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 i ...

IEEE2020

Evaluating salient object detection in natural images with multiple objects having multi-level saliency

Sabine Süsstrunk, Gökhan Yildirim

Salient object detection is evaluated using binary ground truth (GT) with the labels being salient object class and background. In this study, the authors corroborate based on three subjective experiments on a novel image dataset that objects in natural im ...

2020

Kernel Modeling Super-Resolution on Real Low-Resolution Images

Sabine Süsstrunk, Ruofan Zhou

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 per ...

IEEE COMPUTER SOC2019

Deep Feature Factorization For Content-Based Image Retrieval And Localization

Sabine Süsstrunk, Edo Collins

State of the art content-based image retrieval algorithms owe their excellent performance to the rich semantics encoded in the deep activations of a convolutional neural network. The difference between these algorithms lies mostly in how activations are co ...

IEEE2019

Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning

Sabine Süsstrunk, Majed El Helou

Blind and universal image denoising consists of 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 ...

2019

AR in VR: Simulating augmented reality glass for image fusion

Sabine Süsstrunk, Fayez Lahoud

Developing an augmented reality (AR) system involves a multitude of interconnected algorithms such as image fusion, camera synchronization and calibration, and brightness control, each having diverse parameters. This abundance of features, while beneficial ...

2019

Solving the Depth Ambiguity in Single-Perspective Images

Sabine Süsstrunk, Marjan Shahpaski, Majed El Helou

Scene depth estimation is gaining in importance as more and more AR/VR and robot vision applications are developed. Conventional depth-from-defocus techniques can passively provide depth maps from a single image. This is especially advantageous for moving ...

2019

Deep Semantic Segmentation Using Nir As Extra Physical Information

Sabine Süsstrunk, Siavash Arjomand Bigdeli

Deep neural networks for semantic segmentation are most often trained with RGB color images, which encode the radiation visible to the human eyes. In this paper, we study if additional physical scene information, specifically Near-Infrared (NIR) images, im ...

IEEE2019

A comparative study on wavelets and residuals in deep super resolution

Sabine Süsstrunk, Ruofan Zhou, Fayez Lahoud, Majed El Helou

Despite the advances in single-image super resolution using deep convolutional networks, the main problem remains unsolved: recovering fine texture details. Recent works in super resolution aim at modifying the training of neural networks to enable the rec ...

2019

Drone Shadow Tracking

Sabine Süsstrunk, Ruofan Zhou, Majed El Helou

Aerial videos taken by a drone not too far above the surface may contain the drone's shadow projected on the scene. This deteriorates the aesthetic quality of videos. With the presence of other shadows, shadow removal cannot be directly applied, and the sh ...

2019

Multi-Modal Spectral Image Super-Resolution

Sabine Süsstrunk, Ruofan Zhou, Fayez Lahoud

Recent advances have shown the great power of deep convolutional neural networks (CNN) to learn the relationship between low and high-resolution image patches. However, these methods only take a single-scale image as input and require large amount of data ...

Springer2019

Mirror, Mirror, on the Wall, Who’s Got the Clearest Image of Them All?—A Tailored Approach to Single Image Reflection Removal

Sabine Süsstrunk, Georg Maierhofer

Removing reflection artefacts from a single image is a problem of both theoretical and practical interest, which still presents challenges because of the massively ill-posed nature of the problem. In this paper, we propose a technique based on a novel opti ...

2019

Synthetic electronic video containing a hidden image

Roger Hersch, Sabine Süsstrunk, Mahmut Sami Arpa

We present a method for hiding images in synthetic videos and reveal them by temporal averaging. We developed a visual masking method that hides the input image both spatially and temporally. Our masking approach consists of temporal and spatial pixel by p ...

2019

Zero-Learning Fast Medical Image Fusion

Sabine Süsstrunk, Fayez Lahoud

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 prop ...

2019

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