{"paper":{"title":"Blind Deconvolution Meets Blind Demixing: Algorithms and Performance Bounds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Shuyang Ling, Thomas Strohmer","submitted_at":"2015-12-24T07:07:02Z","abstract_excerpt":"Suppose that we have $r$ sensors and each one intends to send a function $\\boldsymbol{g}_i$ (e.g.\\ a signal or an image) to a receiver common to all $r$ sensors. During transmission, each $\\boldsymbol{g}_i$ gets convolved with a function $\\boldsymbol{f}_i$. The receiver records the function $\\boldsymbol{y}$, given by the sum of all these convolved signals. When and under which conditions is it possible to recover the individual signals $\\boldsymbol{g}_i$ and the blurring functions $\\boldsymbol{f}_i$ from just one received signal $\\boldsymbol{y}$? This challenging problem, which intertwines bli"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.07730","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}