Search Results for author:
Found 32 papers, 9 papers with code
Leveraging edge detection and neural networks for better UAV localization
Here, we demonstrate that the performance of these methods can be significantly enhanced by preprocessing the images to extract their edges, which exhibit robustness to seasonal and illumination variations.
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.
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.
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.
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.
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.
Optimal and Efficient Binary Questioning for Human-in-the-Loop Annotation
The problem is framed as the full annotation of a binary classification dataset with the minimal number of yes/no questions when a predictor is available.
IAdet: Simplest human-in-the-loop object detection
This work proposes a strategy for training models while annotating data named Intelligent Annotation (IA).
Collaborative Blind Image Deblurring
Blurry images usually exhibit similar blur at various locations across the image domain, a property barely captured in nowadays blind deblurring neural networks.
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.
Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery
Image resolution is an important criterion for many applications based on satellite imagery.
On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution
In this work, we study the problem of single-image super-resolution (SISR) of Sentinel-2 imagery.
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.).
Fast Two-step Blind Optical Aberration Correction
The optics of any camera degrades the sharpness of photographs, which is a key visual quality criterion.
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.
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.
Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?
In this paper, we propose a study aiming to determine which is the best approach to train denoising networks for real raw videos: supervision on synthetic realistic data or self-supervision on real data.
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.
Automatic Stockpile Volume Monitoring using Multi-view Stereo from SkySat Imagery
The refined RPCs are then used to reconstruct multiple consistent Digital Surface Models (DSMs) from different stereo pairs at each date.
Robust Rational Polynomial Camera Modelling for SAR and Pushbroom Imaging
The Rational Polynomial Camera (RPC) model can be used to describe a variety of image acquisition systems in remote sensing, notably optical and Synthetic Aperture Radar (SAR) sensors.
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.
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.
Joint Demosaicking and Denoising Benefits from a Two-stage Training Strategy
Image demosaicking and denoising are the first two key steps of the color image production pipeline.
A Review of an Old Dilemma: Demosaicking First, or Denoising First?
In this paper, we review the main variants of these strategies and carry-out an extensive evaluation to find the best way to reconstruct full color images from a noisy mosaic.
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.
Assessing the Sharpness of Satellite Images: Study of the PlanetScope Constellation
New micro-satellite constellations enable unprecedented systematic monitoring applications thanks to their wide coverage and short revisit capabilities.
Efficient Blind Deblurring under High Noise Levels
In this work, we first show that current state-of-the-art kernel estimation methods based on the $\ell_0$ gradient prior can be adapted to handle high noise levels while keeping their efficiency.
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.
Modeling Realistic Degradations in Non-blind Deconvolution
We show that accurately modeling a more realistic image acquisition pipeline leads to significant improvements, both in terms of image quality and PSNR.
Accurate Motion Estimation through Random Sample Aggregated Consensus
This yields RANSAAC, a framework that improves systematically over RANSAC and its state-of-the-art variants by statistically aggregating hypotheses.
