{"publication":"IEEE Transactions on Signal Processing","language":[{"iso":"eng"}],"publication_status":"published","title":"Relative optimization for blind deconvolution","date_published":"2005-05-23T00:00:00Z","oa_version":"None","publisher":"Institute of Electrical and Electronics Engineers (IEEE)","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"mla":"Bronstein, Alex M., et al. “Relative Optimization for Blind Deconvolution.” IEEE Transactions on Signal Processing, vol. 53, no. 6, Institute of Electrical and Electronics Engineers (IEEE), 2005, pp. 2018–26, doi:10.1109/tsp.2005.847822.","chicago":"Bronstein, Alex M., M.M. Bronstein, and M. Zibulevsky. “Relative Optimization for Blind Deconvolution.” IEEE Transactions on Signal Processing. Institute of Electrical and Electronics Engineers (IEEE), 2005. https://doi.org/10.1109/tsp.2005.847822.","apa":"Bronstein, A. M., Bronstein, M. M., & Zibulevsky, M. (2005). Relative optimization for blind deconvolution. IEEE Transactions on Signal Processing. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tsp.2005.847822","ista":"Bronstein AM, Bronstein MM, Zibulevsky M. 2005. Relative optimization for blind deconvolution. IEEE Transactions on Signal Processing. 53(6), 2018–2026.","short":"A.M. Bronstein, M.M. Bronstein, M. Zibulevsky, IEEE Transactions on Signal Processing 53 (2005) 2018–2026.","ama":"Bronstein AM, Bronstein MM, Zibulevsky M. Relative optimization for blind deconvolution. IEEE Transactions on Signal Processing. 2005;53(6):2018-2026. doi:10.1109/tsp.2005.847822","ieee":"A. M. Bronstein, M. M. Bronstein, and M. Zibulevsky, “Relative optimization for blind deconvolution,” IEEE Transactions on Signal Processing, vol. 53, no. 6. Institute of Electrical and Electronics Engineers (IEEE), pp. 2018–2026, 2005."},"abstract":[{"lang":"eng","text":"We propose a relative optimization framework for quasi-maximum likelihood (QML) blind deconvolution and the relative Newton method as its particular instance. Special Hessian structure allows fast Newton system construction and solution, resulting in a fast-convergent algorithm with iteration complexity comparable to that of gradient methods. We also propose the use of rational infinite impulse response (IIR) restoration kernels, which constitute a richer family of filters than the traditionally used finite impulse response (FIR) kernels. We discuss different choices of nonlinear functions that are suitable for deconvolution of super- and sub-Gaussian sources and formulate the conditions under which the QML estimation is stable. Simulation results demonstrate the efficiency of the proposed methods."}],"issue":"6","quality_controlled":"1","intvolume":" 53","article_processing_charge":"No","status":"public","_id":"18417","day":"23","date_created":"2024-10-15T11:20:55Z","type":"journal_article","scopus_import":"1","month":"05","date_updated":"2024-12-12T12:19:43Z","doi":"10.1109/tsp.2005.847822","extern":"1","year":"2005","publication_identifier":{"issn":["1053-587X"]},"page":"2018-2026","author":[{"first_name":"Alexander","orcid":"0000-0001-9699-8730","full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein"},{"first_name":"M.M.","full_name":"Bronstein, M.M.","last_name":"Bronstein"},{"last_name":"Zibulevsky","first_name":"M.","full_name":"Zibulevsky, M."}],"volume":53}