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.
Modern Earth observation satellites capture multi-exposure bursts of push-frame images that can be super-resolved via computational means.
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.
We introduce the Satellite Neural Radiance Field (Sat-NeRF), a new end-to-end model for learning multi-view satellite photogrammetry in the wild.
The refined RPCs are then used to reconstruct multiple consistent Digital Surface Models (DSMs) from different stereo pairs at each date.
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.
Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images.
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.
Moreover, it is very difficult to change this order, because once the image is demosaicked, the statistical properties of the noise will be changed dramatically.
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.
We propose a self-supervised approach for training multi-frame video denoising networks.
Due to the unavailability of ground truth data these networks cannot be currently trained using real RAW images.
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.
New micro-satellite constellations enable unprecedented systematic monitoring applications thanks to their wide coverage and short revisit capabilities.
Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available.
We show that accurately modeling a more realistic image acquisition pipeline leads to significant improvements, both in terms of image quality and PSNR.
This yields RANSAAC, a framework that improves systematically over RANSAC and its state-of-the-art variants by statistically aggregating hypotheses.