Search Results for author: Ruofan Zhou

Found 9 papers, 8 papers with code

VIDIT: Virtual Image Dataset for Illumination Transfer

2 code implementations11 May 2020 Majed El Helou, Ruofan Zhou, Johan Barthas, Sabine Süsstrunk

Deep image relighting is gaining more interest lately, as it allows photo enhancement through illumination-specific retouching without human effort.

Domain Adaptation Image Relighting

Divergence-Based Adaptive Extreme Video Completion

no code implementations14 Apr 2020 Majed El Helou, Ruofan Zhou, Frank Schmutz, Fabrice Guibert, Sabine Süsstrunk

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.

Motion Estimation

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

1 code implementation12 Mar 2020 Ruofan Zhou, Majed El Helou, Daniel Sage, Thierry Laroche, Arne Seitz, Sabine Süsstrunk

To study JDSR on microscopy data, we propose such a novel JDSR dataset, Widefield2SIM (W2S), acquired using a conventional fluorescence widefield and SIM imaging.

Denoising Super-Resolution

Kernel Modeling Super-Resolution on Real Low-Resolution Images

1 code implementation ICCV 2019 Ruofan Zhou, Sabine Susstrunk

To improve generalization and robustness of deep super-resolution CNNs on real photographs, we present a kernel modeling super-resolution network (KMSR) that incorporates blur-kernel modeling in the training.

Image Super-Resolution

Drone Shadow Tracking

1 code implementation20 May 2019 Xiaoyan Zou, Ruofan Zhou, Majed El Helou, Sabine Süsstrunk

In this paper, we incorporate knowledge of the shadow's physical properties, in the form of shadow detection masks, into a correlation-based tracking algorithm.

Shadow Detection Shadow Removal

Deep Residual Network for Joint Demosaicing and Super-Resolution

1 code implementation19 Feb 2018 Ruofan Zhou, Radhakrishna Achanta, Sabine Süsstrunk

By training on high-quality samples, our deep residual demosaicing and super-resolution network is able to recover high-quality super-resolved images from low-resolution Bayer mosaics in a single step without producing the artifacts common to such processing when the two operations are done separately.

Demosaicking SSIM +1

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