Image and Visual Representation Lab

Welcome to IVRL

IVRL Logo

 

The Image and Visual Representation Lab (IVRL) performs research that is primarily focused on the capture, analysis, and reproduction of color images. Aiming to improve everyone’s photographic experience, we develop algorithms and systems that help us understand, process, and measure images.

Our research areas are computational photography, color image processing, computer vision, and image quality.

News:

Peter Grönquist and Yufan Ren in Singapore © AI Singapore / EPFL 2022

“Deepfake generation and detection is like an arms race”

— Two EPFL computer scientists have taken home a major prize in Singapore’s Trusted Media Challenge, a five-month long competition aimed at cracking the code of deepfakes.

© 2022 CVPR

Our paper on self supervised learning (SSL) accepted to CVPR 2022

— The paper "Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy" by Tong Zhang, Congpei Qiu, Wei Ke, Sabine Süsstrunk, Mathieu Salzmann is accepted to CVPR2022.

© 2022 CVPR

Our paper on multitask transformer will appear in CVPR 2022

— The paper "MulT: An End-to-End Multitask Learning Transformer" by Deblina Bhattacharjee, Tong Zhang, Sabine Süsstrunk and Mathieu Salzmann is accepted to CVPR2022.

© 2022 EPFL

BIGPrior is published in IEEE Transactions on Image Processing

— BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration by Majed El Helou and Sabine Süsstrunk published in IEEE Transactions on Image Processing.

© 2022 WACV

Estimating Image Depth in the Comics Domain

— Estimating Image Depth in the Comics Domain by Deblina Bhattacharjee, Martin Everaert, Mathieu Salzmann, Sabine Süsstrunk accepted into Winter Conference on Applications of Computer Vision (WACV 2022). 

© 2022 IEEE

IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

— Training Provably Robust Models by Polyhedral Envelope Regularization by Chen Liu, Mathieu Salzmann and Sabine Süsstrunk published in IEEE Transactions on Neural Networks and Learning Systems (TNNLS).

© Olivier Stucky, 2021

Transfer, Reconfiguration and Digital Transition in Graphic Narrative

— Bahar Aydemir and Raphaël Baroni presented their work "Beyond the Shadow of the Z: How We Read European Bande Dessinée" in The international conference “Transfer, Reconfiguration and Digital Transition in Graphic Narratives”.

© 2021 EPFL

Video compression patent and licensing

— We released an online demo of our extreme video completion method. Our technology is very energy and time-efficient on the encoder side (we require practically 0 computation and no specialized hardware like typical DCT-based encoders), it can reach extreme compression rates with complete rate flexibility and can achieve better visual reconstruction at these rates compared with the MPEG-4 H264 standard. This makes it ideal for instance for video-transmitting IoT devices, surveillance cameras, drone transmission, or emergency deployment transmission where bandwidth is very limited. Users can visualize different results and test with their own videos too on: adefan.epfl.ch For the more technically curious readers, we have an associated publication in the leading signal processing conference IEEE ICASSP that can be accessed here: https://infoscience.epfl.ch/record/277003/

© 2021 CVPR

Depth-guided image relighting NTIRE workshop at CVPR

— IVRL members Majed El Helou and Ruofan Zhou co-organised this workshop, using the VIDIT dataset with geometry information. You can access the challenge paper here. 250 participants registered from various research labs, companies, and universities internationally. 

© 2021 EPFL

We released the image relighting dataset "VIDIT"

— We released depth information for our image relighting dataset VIDIT. It is used for the NTIRE workshop competitions in CVPR 2021 Image relighting is fundamental for domain adaptation (data augmentation, normalization), photo montage and photo manipulation for aesthetics.

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