Galaxy image simplification using Generative AI
- URL: http://arxiv.org/abs/2507.11692v1
- Date: Tue, 15 Jul 2025 19:48:09 GMT
- Title: Galaxy image simplification using Generative AI
- Abstract summary: We introduce a new approach to galaxy image analysis that is based on generative AI.<n>The method simplifies the galaxy images and automatically converts them into a skeletonized" form.<n>We demonstrate the method by applying it to galaxy images acquired by the DESI Legacy Survey.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Modern digital sky surveys have been acquiring images of billions of galaxies. While these images often provide sufficient details to analyze the shape of the galaxies, accurate analysis of such high volumes of images requires effective automation. Current solutions often rely on machine learning annotation of the galaxy images based on a set of pre-defined classes. Here we introduce a new approach to galaxy image analysis that is based on generative AI. The method simplifies the galaxy images and automatically converts them into a ``skeletonized" form. The simplified images allow accurate measurements of the galaxy shapes and analysis that is not limited to a certain pre-defined set of classes. We demonstrate the method by applying it to galaxy images acquired by the DESI Legacy Survey. The code and data are publicly available. The method was applied to 125,000 DESI Legacy Survey images, and the catalog of the simplified images is publicly available.
Related papers
- Amplifying the imaging power of digital sky surveys with space telescopes data and generative AI [0.0]
We utilize generative AI to elevate the quality of galaxy images taken by ground-based telescopes to the level of details enabled by space telescopes.<n>The solution is based on the nature of galaxy shapes, allowing generative AI trained on space-based images to convert weak signal into detailed and clear galaxy images.
arXiv Detail & Related papers (2026-08-21T01:58:22Z) - SimpleProc: Fully Procedural Synthetic Data from Simple Rules for Multi-View Stereo [59.11389699606293]
We show that we can generate effective training data using SimpleProc: a new, fully procedural generator driven by a very small set of rules.<n>At a modest scale of 8,000 images, our approach achieves superior results compared to manually curated images from games and real-world objects.<n>When scaled to 352,000 images, our method yields performance comparable to--and in several benchmarks, exceeding--models trained on over 692,000 manually curated images.
arXiv Detail & Related papers (2026-04-06T17:59:05Z) - Exploring Non-contrastive Self-supervised Representation Learning for Image-based Profiling [80.09819072780193]
SSLProfiler is a non-contrastive SSL framework specifically designed for cell profiling.<n>We introduce specialized data augmentation and representation post-processing methods tailored to cell images.<n>With these improvements, SSLProfiler won the Cell Line Transferability challenge at CVPR 2025.
arXiv Detail & Related papers (2025-06-17T07:25:57Z) - Self-Supervised Learning for Detecting AI-Generated Faces as Anomalies [58.11545090128854]
We describe an anomaly detection method for AI-generated faces by leveraging self-supervised learning of camera-intrinsic and face-specific features purely from photographic face images.<n>The success of our method lies in designing a pretext task that trains a feature extractor to rank four ordinal exchangeable image file format (EXIF) tags and classify artificially manipulated face images.
arXiv Detail & Related papers (2025-01-04T06:23:24Z) - GalaxAlign: Mimicking Citizen Scientists' Multimodal Guidance for Galaxy Morphology Analysis [4.557098550366064]
Galaxy morphology analysis involves studying galaxies based on their shapes and structures.<n>Existing methods either directly train domain-specific foundation models on large, annotated datasets or fine-tune vision foundation models on a smaller set of images.<n>We introduce GalaxAlign, a multimodal approach inspired by how citizen scientists identify galaxies in astronomical images.
arXiv Detail & Related papers (2024-11-29T05:10:47Z) - Galaxy Classification: A machine learning approach for classifying
shapes using numerical data [0.0]
We present a machine learning model for galaxy classification using numerical data from the Galaxy Zoo project.
Our results show that our model achieves high accuracy in classifying galaxies and has the potential to significantly enhance our understanding of the formation and evolution of galaxies.
arXiv Detail & Related papers (2023-11-30T20:47:16Z) - Detecting Generated Images by Real Images Only [64.12501227493765]
Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training.
This paper approaches the generated image detection problem from a new perspective: Start from real images.
By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace.
arXiv Detail & Related papers (2023-11-02T03:09:37Z) - Spiral-Elliptical automated galaxy morphology classification from
telescope images [0.40792653193642503]
We develop two novel galaxy morphology statistics, descent average and descent variance, which can be efficiently extracted from telescope galaxy images.
We utilize the galaxy image data from the Sloan Digital Sky Survey to demonstrate the effective performance of our proposed image statistics.
arXiv Detail & Related papers (2023-10-10T22:36:52Z) - Kosmos-G: Generating Images in Context with Multimodal Large Language Models [117.0259361818715]
Current subject-driven image generation methods require test-time tuning and cannot accept interleaved multi-image and text input.
This paper presents Kosmos-G, a model that leverages the advanced multimodal perception capabilities of Multimodal Large Language Models.
Kosmos-G demonstrates an impressive capability of zero-shot subject-driven generation with interleaved multi-image and text input.
arXiv Detail & Related papers (2023-10-04T17:28:44Z) - Astroformer: More Data Might not be all you need for Classification [0.0]
We introduce Astroformer, a method to learn from less amount of data.
Our approach sets a new state-of-the-art on predicting galaxy morphologies from images on the Galaxy10 DECals dataset.
arXiv Detail & Related papers (2023-04-03T09:38:05Z) - InvGAN: Invertible GANs [88.58338626299837]
InvGAN, short for Invertible GAN, successfully embeds real images to the latent space of a high quality generative model.
This allows us to perform image inpainting, merging, and online data augmentation.
arXiv Detail & Related papers (2021-12-08T21:39:00Z) - Morphological classification of astronomical images with limited
labelling [0.0]
We propose an effective semi-supervised approach for galaxy morphology classification task, based on active learning of adversarial autoencoder (AAE) model.
For a binary classification problem (top level question of Galaxy Zoo 2 decision tree) we achieved accuracy 93.1% on the test part with only 0.86 millions markup actions.
Our best model with additional markup accuracy of 95.5%.
arXiv Detail & Related papers (2021-04-27T19:26:27Z) - DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts
using Deep Learning [70.80563014913676]
We investigate the use of convolutional neural networks (CNNs) for the problem of separating low-surface-brightness galaxies from artifacts in survey images.
We show that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
arXiv Detail & Related papers (2020-11-24T22:51:08Z) - A catalog of broad morphology of Pan-STARRS galaxies based on deep
learning [0.0]
We describe the design and implementation of a data analysis process for automatic broad morphology annotation of galaxies.
The process is based on filters followed by a two-step convolutional neural network (CNN) classification.
Results are evaluated for accuracy by comparison to the annotation of Pan-STARRS galaxies.
arXiv Detail & Related papers (2020-10-12T23:20:35Z)
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.