Search Results for author:
Found 10 papers, 5 papers with code
Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites
Modern Earth observation satellites capture multi-exposure bursts of push-frame images that can be super-resolved via computational means.
Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolution
We argue that in doing so, the challenge ranks the proposed methods not only by their MISR performance, but mainly by the heuristics used to guess which image in the series is the most similar to the high-resolution target.
Self-Supervised training for blind multi-frame video denoising
We propose a self-supervised approach for training multi-frame video denoising networks.
Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw Images
Due to the unavailability of ground truth data these networks cannot be currently trained using real RAW images.
Reducing Anomaly Detection in Images to Detection in Noise
Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image.
Non-Local Video Denoising by CNN
To the best of our knowledge, this is the first successful application of a CNN to video denoising.
Model-blind Video Denoising Via Frame-to-frame Training
Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available.
Image Anomalies: a Review and Synthesis of Detection Methods
We review the broad variety of methods that have been proposed for anomaly detection in images.
A survey of exemplar-based texture synthesis
The two main approaches are statistics-based methods and patch re-arrangement methods.
Brain Tumor Segmentation with Deep Neural Networks
Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN.
Ranked #1 on
Brain Tumor Segmentation
on BRATS-2013 leaderboard
