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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Podrobná bibliografia
Vydané v:	Electronics letters Ročník 57; číslo 22; s. 851 - 853
Hlavní autori:	Lee, Bokyeung, Ku, Bonhwa, Kim, Wanjin, Ko, Hanseok
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Stevenage John Wiley & Sons, Inc 01.10.2021 Wiley
Predmet:	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
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Popis
Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0013-5194 1350-911X
DOI:	10.1049/ell2.12284