CI-1.3
WEIGHTED GRAPH EMBEDDED LOW-RANK PROJECTION LEARNING FOR FEATURE EXTRACTION
Zhuojie Huang, Shuping Zhao, Lunke Fei, Jigang Wu, Guangdong University of Technology, China
Session:
Computational Imaging Methods and Models
Track:
Computational Imaging
Location:
Gather Area H
Presentation Time:
Sun, 8 May, 23:00 - 23:45 China Time (UTC +8)
Sun, 8 May, 15:00 - 15:45 UTC
Sun, 8 May, 15:00 - 15:45 UTC
Session Chair:
Lei Wang, KLASS Engineering and Solutions Pte Ltd
Session CI-1
CI-1.1: BILEVEL LEARNING OF L1 REGULARIZERS WITH CLOSED-FORM GRADIENTS (BLORC)
Avrajit Ghosh, Saiprasad Ravishankar, Michigan State University, United States of America; Michael Mccann, Los Alamos National Laboratory, United States of America
CI-1.2: MULTIBAND IMAGE FUSION WITH CONTROLLABLE ERROR GUARANTEES
Unni V. S., Ruturaj Gavaskar, Kunal Chaudhury, Indian Institute of Science, India
CI-1.3: WEIGHTED GRAPH EMBEDDED LOW-RANK PROJECTION LEARNING FOR FEATURE EXTRACTION
Zhuojie Huang, Shuping Zhao, Lunke Fei, Jigang Wu, Guangdong University of Technology, China
CI-1.4: ADMM-DAD NET: A DEEP UNFOLDING NETWORK FOR ANALYSIS COMPRESSED SENSING
Vasiliki Kouni, George C. Alexandropoulos, National and Kapodistrian University of Athens, Greece; Georgios Paraskevopoulos, National Technical University of Athens, Greece; Holger Rauhut, Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
CI-1.5: HIGH-DIMENSIONAL SPARSE BAYESIAN LEARNING WITHOUT COVARIANCE MATRICES
Alexander Lin, Demba Ba, Harvard University, United States of America; Andrew Song, Massachusetts Institute of Technology, United States of America; Berkin Bilgic, Athinoula A. Martinos Center for Biomedical Imaging, United States of America
CI-1.6: A TRAINABLE BOUNDED DENOISER USING DOUBLE TIGHT FRAME NETWORK FOR SNAPSHOT COMPRESSIVE IMAGING
Baoshun Shi, Yuxin Wang, Qiusheng Lian, Yanshan University, China
CI-1.7: PROGRESSIVE IMAGE SUPER-RESOLUTION VIA NEURAL DIFFERENTIAL EQUATION
Seobin Park, Tae Hyun Kim, Hanyang University, Korea, Republic of