Hierarchical distillation for image compressive sensing reconstruction

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 performa...

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Bibliographic Details
Published in:Electronics letters Vol. 57; no. 22; pp. 851 - 853
Main Authors: Lee, Bokyeung, Ku, Bonhwa, Kim, Wanjin, Ko, Hanseok
Format: Journal Article
Language:English
Published: Stevenage John Wiley & Sons, Inc 01.10.2021
Wiley
Subjects:
Algorithms
Back propagation
Computer vision and image processing techniques
Datasets
Deep learning
Image and video coding
Image reconstruction
Knowledge
Machine learning
Methods
Optical, image and video signal processing
Propagation
Ratios
Sparsity
ISSN:0013-5194, 1350-911X
Online Access:Get full text
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Summary: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.
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ISSN:0013-5194
1350-911X
DOI:10.1049/ell2.12284