{"_id":"6y6uLYAtv6hQM5a5Z","bibbaseid":"lane-methodsformaximumlikelihooddeconvolution-1996","downloads":0,"creationDate":"2017-08-30T17:46:18.932Z","title":"Methods for maximum-likelihood deconvolution","author_short":["Lane, R., G."],"year":1996,"bibtype":"article","biburl":null,"bibdata":{"title":"Methods for maximum-likelihood deconvolution","type":"article","year":"1996","pages":"1992-1998","volume":"13","id":"54583b70-87f5-325a-beab-391fc3819a52","created":"2017-06-23T21:47:02.040Z","file_attached":"true","profile_id":"d9ca9665-cda3-3617-b3e8-ecfc7a8d9739","group_id":"5a95a0b6-1946-397c-b16a-114c6fdf3127","last_modified":"2017-06-23T21:47:26.872Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Lane1996","folder_uuids":"a55b3938-9553-4705-9661-67c5f054d79a","private_publication":false,"abstract":"An alternative approach to the maximum-likelihood solution of deconvolution problems is presented. The re- sulting algorithms are faster converging than the conventional Richardson – Lucy and CLEAN algorithms, as well as being more flexible when one is dealing with different types of noise. The performance of the algo- rithms on both Poisson and independent sensor noise is quantified.","bibtype":"article","author":"Lane, R G","journal":"Journal of the Optical Society of America","number":"10","bibtex":"@article{\n title = {Methods for maximum-likelihood deconvolution},\n type = {article},\n year = {1996},\n pages = {1992-1998},\n volume = {13},\n id = {54583b70-87f5-325a-beab-391fc3819a52},\n created = {2017-06-23T21:47:02.040Z},\n file_attached = {true},\n profile_id = {d9ca9665-cda3-3617-b3e8-ecfc7a8d9739},\n group_id = {5a95a0b6-1946-397c-b16a-114c6fdf3127},\n last_modified = {2017-06-23T21:47:26.872Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Lane1996},\n folder_uuids = {a55b3938-9553-4705-9661-67c5f054d79a},\n private_publication = {false},\n abstract = {An alternative approach to the maximum-likelihood solution of deconvolution problems is presented. The re- sulting algorithms are faster converging than the conventional Richardson – Lucy and CLEAN algorithms, as well as being more flexible when one is dealing with different types of noise. The performance of the algo- rithms on both Poisson and independent sensor noise is quantified.},\n bibtype = {article},\n author = {Lane, R G},\n journal = {Journal of the Optical Society of America},\n number = {10}\n}","author_short":["Lane, R., G."],"urls":{"Paper":"http://bibbase.org/service/mendeley/d9ca9665-cda3-3617-b3e8-ecfc7a8d9739/file/63aef730-58cc-19f5-3916-d161ae580865/1996-Methods_for_maximum-likelihood_deconvolution.pdf.pdf"},"bibbaseid":"lane-methodsformaximumlikelihooddeconvolution-1996","role":"author","downloads":0},"search_terms":["methods","maximum","likelihood","deconvolution","lane"],"keywords":[],"authorIDs":[]}