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.
|Number of pages||3|
|Publication status||Published - 2021 Oct|
- Computer vision and image processing techniques
- Image and video coding
- Optical, image and video signal processing
ASJC Scopus subject areas
- Electrical and Electronic Engineering