Separable joint blind deconvolution and demixing

Dana Weitzner, Raja Giryes

Research output: Contribution to journalArticlepeer-review


Blind deconvolution and demixing is the problem of reconstructing convolved signals and kernels from the sum of their convolutions. This problem arises in many applications, such as blind MIMO. This work presents a separable approach to blind deconvolution and demixing via convex optimization. Unlike previous works, our formulation allows separation into smaller optimization problems, which significantly improves complexity. We develop recovery guarantees, which comply with those of the original non-separable problem, and demonstrate the method performance under several normalization constraints.

Original languageEnglish
Article number9337933
Pages (from-to)657-671
Number of pages15
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number3
StatePublished - Apr 2021


  • Blind deconvolution
  • Demixing
  • Low-rank


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