{
  "annotation_id": "de4fd2a0-84e7-4942-aa9f-b3c7f994b3b2",
  "date_created": "2026-03-02T18:00:46.592000Z",
  "date_modified": "2026-03-02T18:00:46.592000Z",
  "file_hash": "cf70318353e0779f45c50b198b5ba836b6d95eb0e0fd48c7bc4515b5e87b2421",
  "private": false,
  "record": {
    "abstract": "In this contribution, we consider the problem of blind source separation in a\nBayesian estimation framework. The wavelet representation allows us to assign\nan adequate prior distribution to the wavelet coefficients of the sources. MCMC\nalgorithms are implemented to test the validity of the proposed approach, and\nthe non linear approximation of the wavelet transform is exploited to aleviate\nthe algorithm.",
    "arxiv_id": "physics/0311033",
    "authors": [
      "Mahieddine M. Ichir",
      "Ali Mohammad-Djafari"
    ],
    "categories": [
      "physics.data-an"
    ],
    "doi": "10.1063/1.1751384",
    "title": "Bayesian Wavelet Based Signal and Image Separation",
    "url": "https://arxiv.org/abs/physics/0311033"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "be3b5f66-fcf4-4580-a7ca-3b510b2d1fe2",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}