UniverLab · open laboratoryEXP-006
Astro Denoise
Frontier knowledge hides behind the noise…
astro-denoise is a research proposal for a modular, reproducible benchmark of noise-reduction methods on simulated Vera Rubin Observatory (LSST DC2) images. Any method — classical filter, trained network — plugs into the same protocol and runs on the same patches, and is scored not by how clean the image looks, but by what it does to the science: the completeness and purity of the faint-source catalog, compared against the DC2 truth catalog. BM3D and a U-Net are the first two references being explored — the platform is designed to grow as more methods are added.
noise reduction → a benchmark scored by the science → comparable methods
EXP-006 · research · · roadmap: ·
A master's thesis in progress.
This began as a master's thesis and is still taking shape. The ecosystem has worked since July 2026: an orchestrator with a common command-line protocol (FITS image in and out, metadata in JSON), a no-processing control condition, and two separately packaged methods, BM3D for the classical family and a U-Net for deep learning. The experiments run in the open, right here, but there is work ahead before this is a finished benchmark.
Benchmarking noise-reduction methods on simulated DC2 images of the Vera Rubin Observatory
Develop a reproducible benchmarking platform that compares noise-reduction methods on simulated DC2 images of the Vera Rubin Observatory, evaluating for each method the recovery of faint sources, the resulting image quality and its computational cost.
- 1completeA modular, extensible ecosystem with a common command-line protocol, where each method is packaged separately with its own environment and a new method joins without changing the orchestrator.
- 2in progressReliable source-recovery metrics (completeness, purity, completeness by magnitude and photometric accuracy, by one-to-one matching against the DC2 truth catalogue) and image-quality metrics (PSNR, SSIM and background noise), each checked against at least two independent sources in the literature.
- 3in progressTools that measure each method's computational cost (time per region, peak memory and regions per unit of time) from outside its process, reported with a description of the hardware.
- 4in progressThe methods under test: BM3D, and a U-Net trained only on DC2 regions outside the evaluation set, with its training recipe and weights published.
- 5pendingA reproducible benchmark on a curated, versioned DESC DC2 dataset of multiband regions from several sky areas; each version is identified by checksums and declares its regions, and every result is reported against one version.
- UniverLab/astro-denoiseThe bench: orchestrator, science metrics and the control condition.
- UniverLab/astro-denoise-bm3dBM3D, the classical baseline.
- UniverLab/astro-denoise-unetThe U-Net, the deep-learning baseline.
- Q1Can a denoiser raise completeness without hurting purity — or does it invent sources that were never there?
- Q2Which family of methods — classical filters or learned models — is more likely to help at LSST DC2 depth, and by how much?
- Q3Can the whole benchmark be repeated with the same scientific results on the same version of data and code?
Research
Research artifacts that cut across the lab’s experiments — papers, datasets, benchmarks, models. The first entries come from this track; the list grows only as work is actually published.
- in preparation
Papers
Publications and preprints produced by lab experiments.
- in preparation
Datasets
Open datasets, starting with astro-denoise benchmark data.
- in preparation
Benchmarks
Reproducible benchmark suites with documented assumptions.
- in preparation
Models
Trained models released alongside their training recipes.
Star chart: Yale Bright Star Catalogue (CDS V/50); constellation lines from d3-celestial by Olaf Frohn (BSD-3-Clause).
FAQ
- What is astro-denoise?
- A research project benchmarking noise-reduction methods on simulated Vera Rubin Observatory (LSST DC2) images. It compares three conditions: a no-processing control, BM3D as the classical baseline and a U-Net as the deep-learning baseline. The goal is to recover faint galaxies without inventing ones that were never there — scored by science recovery (completeness + purity), not visual quality.
- What is noise reduction in astronomy?
- Astronomical images contain unavoidable noise from the detector and sky. Noise reduction aims to lower this noise while preserving real sources. The risk: a method can smooth out real faint galaxies or invent fake ones that were never there.
- What is LSST?
- The Legacy Survey of Space and Time — a 10-year astronomical survey conducted by the Vera Rubin Observatory in Chile. It will image the entire visible sky repeatedly, producing the deepest wide-field catalog of galaxies, stars, and transient events ever made.
- What is DC2?
- Data Challenge 2 — a simulated dataset that mimics what LSST will produce, created by the Dark Energy Science Collaboration (DESC). It includes realistic noise, PSF, and atmospheric effects, with a truth catalog telling you exactly which sources are real.