Hierarchical distillation for image compressive sensing reconstruction. Issue 22 (26th July 2021)

• Record Type: Journal Article
• Authors: Lee, Bokyeung; Ku, Bonhwa; Kim, Wanjin; Ko, Hanseok
• Is Part Of: Electronics letters. Volume 57:Issue 22(2021)
• Page Start: 851
• Page End: 853
• Publication Date: 2021-07-26
• Subjects: Optical, image and video signal processing -- Image and video coding -- Computer vision and image processing techniques; Electronics -- Periodicals; 621.381
• DOI: 10.1049/ell2.12284
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract: 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.