{
  "abstract": "We present a deep-learning framework that performs 4.6x spatial super-resolution from WISE W1 (3.4 micron) toward Spitzer IRAC Ch1 (3.6 micron), and characterize its behavior on the COSMOS field. We train on ~390,000 paired cutouts drawn uniformly within the WISE/Spitzer overlap and report all metrics on a held-out test set of 83,592 cutouts. The framework uses a convolutional neural network trained with a loss function that emphasizes accurate recovery of sources in crowded fields. The model recovers the total flux of the central source in a fixed aperture to a median relative error of 11%, a factor of ~2 better than the interpolation baselines; the gain reaches ~3x on the faintest quartile. The brightness dependence is monotonic: the aperture integrated flux error decreases from 13% on the faintest quartile to 8% on the brightest. At the 3-5 arcsec separations where WISE blends sources that Spitzer separates, the model recovers 35% of the source peaks detectable in the Spitzer truth compared with 9% for interpolation. The characteristic failure mode is oversmoothing of source profiles, which biases integrated flux measurements upward; this pattern is qualitatively similar to that of the interpolation baselines but is quantitatively smaller for the trained model. These results suggest genuine resolution enhancement and source deblending, providing a path toward applying super-resolution across the all-sky area that Spitzer could not cover. An appendix replicates the analysis at W2 -> IRAC Ch2 with consistent results; the trained model and code are publicly available.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.14295",
  "author_total": 8,
  "authors": [
    {
      "name": "Saeed Rezaee"
    },
    {
      "name": "Shoubaneh Hemmati"
    },
    {
      "name": "Bahram Mobasher"
    },
    {
      "name": "Naveen A. Reddy"
    },
    {
      "name": "Tara Fetherolf"
    },
    {
      "name": "Alexander de la Vega"
    },
    {
      "name": "Negin Moharrami Allafi"
    },
    {
      "name": "Sina Miri"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7vn14qee586v6.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.14295",
  "fulltext_url": null,
  "id": "7vn14qee586v6",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.14295",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "1707|Advanced Image Processing Techniques"
    ]
  ],
  "openalex": "W7169517383",
  "pdf_url": "https://arxiv.org/pdf/2607.14295",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7vn14qee586v6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:12Z",
      "native_id": "2607.14295",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.14295"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Enhancing WISE Infrared Imaging to Spitzer Resolution Using Deep Learning Super-Resolution",
  "title_text": "Enhancing WISE Infrared Imaging to Spitzer Resolution Using Deep Learning Super-Resolution",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:12Z",
  "url": "https://research.draconitic.com/paper/7vn14qee586v6",
  "venue": "arXiv",
  "versions": []
}