{
  "id": 18230,
  "title": "What to Remove, What to Preserve: Dual-Ambiguity Rectification for All-in-One Image Restoration",
  "url": "https://urgent.news/2026/07/30/what-to-remove-what-to-preserve-dual-ambiguity-rectification-for-all",
  "topic": "science",
  "section": "Science",
  "published": "2026-07-30T17:01:24.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2607.28526v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "All-in-one image restoration aims to handle diverse degradations within a unified framework. Existing methods commonly encode heterogeneous degradation conditions in a shared latent space, where degradation-related cues and scene content can remain entangled. We characterize the resulting challenge as dual ambiguity: semantic ambiguity in channel-wise modulation and spatial ambiguity in…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}