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Found 18 papers, 4 papers with code
On the Convergence of PatchMatch and Its Variants
We also derive more specific bounds for two of these particular cases: the original PatchMatch and Coherency Sensitive Hashing.
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.
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.
Non-Local Video Denoising by CNN
To the best of our knowledge, this is the first successful application of a CNN to video denoising.
Reducing Anomaly Detection in Images to Detection in Noise
Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image.
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.
Robust copy-move forgery detection by false alarms control
Detecting reliably copy-move forgeries is difficult because images do contain similar objects.
Implementation of the VBM3D Video Denoising Method and Some Variants
VBM3D is an extension to video of the well known image denoising algorithm BM3D, which takes advantage of the sparse representation of stacks of similar patches in a transform domain.
GPU acceleration of NL-means, BM3D and VBM3D
Denoising is an essential part of any image- or video-processing pipeline.
Self-Supervised training for blind multi-frame video denoising
We propose a self-supervised approach for training multi-frame video denoising networks.
Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products
Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images.
Sat-NeRF: Learning Multi-View Satellite Photogrammetry With Transient Objects and Shadow Modeling Using RPC Cameras
We introduce the Satellite Neural Radiance Field (Sat-NeRF), a new end-to-end model for learning multi-view satellite photogrammetry in the wild.
Regularization of NeRFs using differential geometry
Neural radiance fields, or NeRF, represent a breakthrough in the field of novel view synthesis and 3D modeling of complex scenes from multi-view image collections.
Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation
The new generation of hyperspectral imagers, such as PRISMA, has improved significantly our detection capability of methane (CH4) plumes from space at high spatial resolution (30m).
Reducing False Alarms in Video Surveillance by Deep Feature Statistical Modeling
Experimental results reveal that the proposed a-contrario validation is able to largely reduce the number of false alarms at both pixel and object levels.
Radar Fields: An Extension of Radiance Fields to SAR
Radiance fields have been a major breakthrough in the field of inverse rendering, novel view synthesis and 3D modeling of complex scenes from multi-view image collections.
Portraying the Need for Temporal Data in Flood Detection via Sentinel-1
Identifying flood affected areas in remote sensing data is a critical problem in earth observation to analyze flood impact and drive responses.
Exploring Robust Features for Few-Shot Object Detection in Satellite Imagery
Moreover, we study the performance of both visual and image-text features, namely DINOv2 and CLIP, including two CLIP models specifically tailored for remote sensing applications.
