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Found 15 papers, 6 papers with code
Fast, nonlocal and neural: a lightweight high quality solution to image denoising
In this letter, we propose a solution to both issues by combining a nonlocal algorithm with a lightweight residual CNN.
On the Importance of Large Objects in CNN Based Object Detection Algorithms
In this paper, we highlight the importance of large objects in learning features that are critical for all sizes.
L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery
Unfortunately the lack of reliable high-resolution (HR) ground truth limits the application of deep learning methods to this task.
Improving Pixel-Level Contrastive Learning by Leveraging Exogenous Depth Information
In this paper we will focus on the depth information, which can be obtained by using a depth estimation network or measured from available data (stereovision, parallax motion, LiDAR, etc.).
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.
GPU acceleration of NL-means, BM3D and VBM3D
Denoising is an essential part of any image- or video-processing pipeline.
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
