Open AccessHierarchical distillation for image compressive sensing reconstruction
Author(s) -
Lee Bokyeung,
Ku Bonhwa,
Kim Wanjin,
Ko Hanseok
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12284
Subject(s) - compressed sensing , distillation , artificial intelligence , computer science , sample (material) , image (mathematics) , iterative reconstruction , deep learning , pattern recognition (psychology) , computer vision , chemistry , chromatography
Compressive sensing (CS) is an effective algorithm for reconstructing images from a small sample of data. CS models combining traditional optimisation‐based CS methods and deep learning have been used to improve image reconstruction performance. However, if the sample ratio is very low, the performance of the CS method combined with deep learning will be unsatisfactory. In this letter, a deep learning‐based CS model incorporating hierarchical knowledge distillation to improve image reconstruction even at varied sample ratios. Compared to the state‐of‐art methods with all compressive sensing ratios, the proposed method improved performance by an average of 0.26 dB without additional trainable parameters.