Search Results for author: Mohammad Saeed Rad

Found 8 papers, 2 papers with code

Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

1 code implementation5 Oct 2021 Devavrat Tomar, Behzad Bozorgtabar, Manana Lortkipanidze, Guillaume Vray, Mohammad Saeed Rad, Jean-Philippe Thiran

Motivated by atlas-based segmentation, we propose a novel volumetric self-supervised learning for data augmentation capable of synthesizing volumetric image-segmentation pairs via learning transformations from a single labeled atlas to the unlabeled data.

Data Augmentation Image Segmentation +7

Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images

no code implementations6 Apr 2021 Mohammad Saeed Rad, Thomas Yu, Behzad Bozorgtabar, Jean-Philippe Thiran

Addressing both issues, we propose a simple yet universal approach to improve the perceptual quality of the HR prediction from a pre-trained SR network on a given LR input by further fine-tuning the SR network on a subset of images from the training dataset with similar patterns of activation as the initial HR prediction, with respect to the filters of a feature extractor.

SSIM Super-Resolution +1

Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution

no code implementations6 Jul 2020 Mohammad Saeed Rad, Thomas Yu, Claudiu Musat, Hazim Kemal Ekenel, Behzad Bozorgtabar, Jean-Philippe Thiran

First, we train a network to transform real LR images to the space of bicubically downsampled images in a supervised manner, by using both real LR/HR pairs and synthetic pairs.

Image Super-Resolution

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution

no code implementations ICCV 2019 Mohammad Saeed Rad, Behzad Bozorgtabar, Urs-Viktor Marti, Max Basler, Hazim Kemal Ekenel, Jean-Philippe Thiran

By benefiting from perceptual losses, recent studies have improved significantly the performance of the super-resolution task, where a high-resolution image is resolved from its low-resolution counterpart.

Image Super-Resolution

Benefiting from Multitask Learning to Improve Single Image Super-Resolution

no code implementations29 Jul 2019 Mohammad Saeed Rad, Behzad Bozorgtabar, Claudiu Musat, Urs-Viktor Marti, Max Basler, Hazim Kemal Ekenel, Jean-Philippe Thiran

Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a challenging problem.

Image Super-Resolution Semantic Segmentation

Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis

no code implementations17 May 2019 Behzad Bozorgtabar, Mohammad Saeed Rad, Hazim Kemal Ekenel, Jean-Philippe Thiran

Moreover, we also conduct experiments on a near-infrared dataset containing facial expression videos of drivers to assess the performance using in-the-wild data for driver emotion recognition.

Attribute Domain Adaptation +3

Learn to synthesize and synthesize to learn

1 code implementation1 May 2019 Behzad Bozorgtabar, Mohammad Saeed Rad, Hazim Kemal Ekenel, Jean-Philippe Thiran

To overcome these shortcomings, we propose attribute guided face image generation method using a single model, which is capable to synthesize multiple photo-realistic face images conditioned on the attributes of interest.

Attribute Data Augmentation +4

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