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Using known noise statistics, we jointly\nlearn the prior and reconstruct images without access to ground-truth data.\nDuring training, we use alternating minimization across an unrolled iterative\nnetwork and jointly solve for the neural network weights and training set image\nreconstructions. At inference, we fix the weights and pass the measurements\nthrough the network. We compare reconstruction performance between unsupervised\nand supervised (i.e. with ground-truth) methods. We hypothesize this technique\ncould be used to learn reconstruction when ground-truth data are unavailable,\nsuch as in high-resolution dynamic MRI.","url_abs":"http://arxiv.org/abs/1910.13110v3","url_pdf":"http://arxiv.org/pdf/1910.13110v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unsupervised-deep-basis-pursuit-learning","repo_url":"https://github.com/utcsilab/deepinpy","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"compressed-sensing","task_name":"compressed sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1910.13110","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.13110"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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