Astronomical Images Quality Assessment with Automated Machine Learning
- URL: http://arxiv.org/abs/2311.10617v1
- Date: Fri, 17 Nov 2023 16:14:11 GMT
- Title: Astronomical Images Quality Assessment with Automated Machine Learning
- Authors: Olivier Parisot, Pierrick Bruneau, Patrik Hitzelberger
- Abstract summary: Electronically Assisted Astronomy consists of capturing deep sky images with a digital camera coupled to a telescope to display views of celestial objects that would have been invisible through direct observation.
This practice generates a large quantity of data, which may then be enhanced with dedicated image editing software after observation sessions.
In this study, we show how Image Quality Assessment can be useful for automatically rating astronomical images, and we also develop a dedicated model by using Automated Machine Learning.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Electronically Assisted Astronomy consists in capturing deep sky images with
a digital camera coupled to a telescope to display views of celestial objects
that would have been invisible through direct observation. This practice
generates a large quantity of data, which may then be enhanced with dedicated
image editing software after observation sessions. In this study, we show how
Image Quality Assessment can be useful for automatically rating astronomical
images, and we also develop a dedicated model by using Automated Machine
Learning.
Related papers
- AstroSpy: On detecting Fake Images in Astronomy via Joint Image-Spectral Representations [31.75799061059914]
The prevalence of AI-generated imagery has raised concerns about the authenticity of astronomical images.
We present AstroSpy, a hybrid model that integrates both spectral and image features to distinguish real from synthetic astronomical images.
arXiv Detail & Related papers (2024-07-09T12:49:44Z) - Image Restoration with Point Spread Function Regularization and Active
Learning [5.575847437953924]
Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae.
varying noise levels and point spread functions can hamper the accuracy and efficiency of information extraction from these images.
We propose a novel image restoration algorithm that connects a deep learning-based restoration algorithm with a high-fidelity telescope simulator.
arXiv Detail & Related papers (2023-10-31T23:16:26Z) - Robustar: Interactive Toolbox Supporting Precise Data Annotation for
Robust Vision Learning [53.900911121695536]
We introduce the initial release of our software Robustar.
It aims to improve the robustness of vision classification machine learning models through a data-driven perspective.
arXiv Detail & Related papers (2022-07-18T21:12:28Z) - Automatic Image Content Extraction: Operationalizing Machine Learning in
Humanistic Photographic Studies of Large Visual Archives [81.88384269259706]
We introduce Automatic Image Content Extraction framework for machine learning-based search and analysis of large image archives.
The proposed framework can be applied in several domains in humanities and social sciences.
arXiv Detail & Related papers (2022-04-05T12:19:24Z) - A Quality Index Metric and Method for Online Self-Assessment of
Autonomous Vehicles Sensory Perception [164.93739293097605]
We propose a novel evaluation metric, named as the detection quality index (DQI), which assesses the performance of camera-based object detection algorithms.
We have developed a superpixel-based attention network (SPA-NET) that utilizes raw image pixels and superpixels as input to predict the proposed DQI evaluation metric.
arXiv Detail & Related papers (2022-03-04T22:16:50Z) - Attention Mechanisms in Computer Vision: A Survey [75.6074182122423]
We provide a comprehensive review of various attention mechanisms in computer vision.
We categorize them according to approach, such as channel attention, spatial attention, temporal attention and branch attention.
We suggest future directions for attention mechanism research.
arXiv Detail & Related papers (2021-11-15T09:18:40Z) - Self-Supervised Steering Angle Prediction for Vehicle Control Using
Visual Odometry [55.11913183006984]
We show how a model can be trained to control a vehicle's trajectory using camera poses estimated through visual odometry methods.
We propose a scalable framework that leverages trajectory information from several different runs using a camera setup placed at the front of a car.
arXiv Detail & Related papers (2021-03-20T16:29:01Z) - Self-Supervised Representation Learning for Astronomical Images [1.0499611180329804]
Self-supervised learning recovers representations of sky survey images that are semantically useful.
We show that our approach can achieve the accuracy of supervised models while using 2-4 times fewer labels for training.
arXiv Detail & Related papers (2020-12-24T03:25:36Z) - Detection of asteroid trails in Hubble Space Telescope images using Deep
Learning [0.0]
We present an application of Deep Learning for the image recognition of asteroid trails in single-exposure photos taken by the Hubble Space Telescope.
Using algorithms based on multi-layered deep Convolutional Neural Networks, we report accuracies of above 80% on the validation set.
arXiv Detail & Related papers (2020-10-29T09:03:18Z) - Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion [51.19260542887099]
We show that self-supervision can be used to learn accurate depth and ego-motion estimation without prior knowledge of the camera model.
Inspired by the geometric model of Grossberg and Nayar, we introduce Neural Ray Surfaces (NRS), convolutional networks that represent pixel-wise projection rays.
We demonstrate the use of NRS for self-supervised learning of visual odometry and depth estimation from raw videos obtained using a wide variety of camera systems.
arXiv Detail & Related papers (2020-08-15T02:29:13Z) - Self-supervised Learning for Astronomical Image Classification [1.2891210250935146]
In Astronomy, a huge amount of image data is generated daily by photometric surveys.
We propose a technique to leverage unlabeled astronomical images to pre-train deep convolutional neural networks.
arXiv Detail & Related papers (2020-04-23T17:32:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.