Technical Program

SSP-O.2: Sparse Signal Processing and Deep Learning II

Symposium: Symposium on Sparse Signal Processing and Deep Learning
Session Type: Oral
Time: Tuesday, November 14, 14:00 - 15:30
Location: Mont-Royal
Session Chair: Chinmay Hegde, Iowa State University
 
14:00 - 14:18
SSP-O.2.1: FAST ADMM SOLVER FOR REWEIGHTED TOTAL VARIATION IMAGE DECONVOLUTION AND INPAINTING
         John Lee; Georgia Institute of Technology
         Christopher Rozell; Georgia Institute of Technology
 
14:18 - 14:36
SSP-O.2.2: HOW TO DEAL WITH MULTI-SOURCE DATA FOR TREE DETECTION BASED ON DEEP LEARNING
         Lionel Pibre; LIRMM laboratory, University of Montpellier / Berger-Levrault Company
         Marc Chaumont; LIRMM laboratory, University of Montpellier / University of Nîmes
         Gérard Subsol; LIRMM laboratory, University of Montpellier / CNRS
         Dino Ienco; IRSTEA
         Mustapha Derras; Berger-Levrault Company
 
14:36 - 14:54
SSP-O.2.3: HYPERSPECTRAL IMAGE FUSION BASED ON NON-FACTORIZATION SPARSE REPRESENTATION AND ERROR MATRIX ESTIMATION
         Xiaolin Han; Tsinghua University
         Jiqiang Luo; Beijing Institute of Technology
         Jing Yu; Beijing University of Technology
         Weidong Sun; Tsinghua University
 
14:54 - 15:12
SSP-O.2.4: JOINT-SPARSE DICTIONARY LEARNING: DENOISING MULTIPLE MEASUREMENT VECTORS
         Prerna Singh; IIITD
         Ramy Hussein; University of British Columbia
         Angshul Majumdar; IIITD
         Rabab Ward; University of British Columbia
 
15:12 - 15:30
SSP-O.2.5: DEMIXING STRUCTURED SUPERPOSITION SIGNALS FROM PERIODIC AND APERIODIC NONLINEAR OBSERVATIONS
         Mohammadreza Soltani; Iowa State University
         Chinmay Hegde; Iowa State University