Publications by Ruofan Zhou | EPFL Graph Search

Adaptive extreme video completion

Sabine Süsstrunk, Radhakrishna Achanta, Ruofan Zhou, Majed El Helou

A computer-implemented video completion method is proposed for reconstructing a sparsely sampled video comprising sparsely sampled video frames comprising sparse sets of picture element intensity values. The method comprises: distributing the sparse sets o ...

2021

NTIRE 2021 Depth Guided Image Relighting Challenge

Sabine Süsstrunk, Radu Timofte, Ruofan Zhou, Majed El Helou

Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multip ...

IEEE COMPUTER SOC2021

Towards Real-World Super-Resolution using Deep Neural Networks

Ruofan Zhou

Image super-resolution reconstructs a higher-resolution image from the observed low-resolution image. In recent years, machine learning models have been widely employed and deep learning networks have achieved state-of-the-art super-resolution performance. ...

EPFL2020

Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

Sabine Süsstrunk, Ruofan Zhou, Majed El Helou

Super-resolution and denoising are ill-posed yet fundamental image restoration tasks. In blind settings, the degradation kernel or the noise level are unknown. This makes restoration even more challenging, notably for learning-based methods, as they tend t ...

2020

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

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

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

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

AIM 2019 Challenge on Image Extreme Super-Resolution: Methods and Results

Radu Timofte, Xin Yang, Jia Yu, Ruofan Zhou, Muhammad Haris, Majed El Helou, Peng Yi, Jiayi Ma, Wei Li, Hanwen Liu, Liying Lu

This paper reviews the AIM 2019 challenge on extreme image super-resolution, the problem of restoring of rich details in a low resolution image. Compared to previous, this challenge focuses on an extreme upscaling factor, x16, and employs the novel DIVerse ...

IEEE COMPUTER SOC2019

Deep Residual Network for Joint Demosaicing and Super- Resolution

Sabine Süsstrunk, Radhakrishna Achanta, Ruofan Zhou

The two classic image restoration tasks, demosaicing and super-resolution, have traditionally always been studied indepen- dently. That is sub-optimal as sequential processing, demosaic- ing and then super-resolution, may lead to amplification of ar- tifac ...

Society for Imaging Science and Technology ( IS&T )2018

NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images

Radu Timofte, Marjan Shahpaski, Ruofan Zhou, Fayez Lahoud, Majed El Helou, Dong Liu, Lei Zhang, Soumik Sarkar, Himanshu Sharma, Bing Zhang, Chang Chen

This paper reviews the first challenge on spectral image reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3- channel RGB image. The challenge was divided into 2 tracks: the “Clean” track sought HS reco ...

2018