Learning Memory Augmented Cascading Network for Compressed Sensing of Images
Abstract
In this paper, we propose a cascading network for compressed sensing of images with progressive reconstruction. Specifically, we decompose the complex reconstruction mapping into the cascade of incremental detail reconstruction (IDR) modules and measurement residual updating (MRU) modules. The IDR module is designed to reconstruct the remaining details from the residual measurement vector, and MRU is employed to update the residual measurement vector and feed it into the next IDR module. The contextual memory module is introduced to augment the capacity of IDR modules, therefore facilitating the information interaction among all the IDR modules. The final reconstruction is calculated by accumulating the outputs of all the IDR modules. Extensive experiments on natural images and magnetic resonance images demonstrate the proposed method achieves better performance against the state-of-the-art methods.
Cite
Text
Chen et al. "Learning Memory Augmented Cascading Network for Compressed Sensing of Images." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58542-6_31Markdown
[Chen et al. "Learning Memory Augmented Cascading Network for Compressed Sensing of Images." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/chen2020eccv-learning-e/) doi:10.1007/978-3-030-58542-6_31BibTeX
@inproceedings{chen2020eccv-learning-e,
title = {{Learning Memory Augmented Cascading Network for Compressed Sensing of Images}},
author = {Chen, Jiwei and Sun, Yubao and Liu, Qingshan and Huang, Rui},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58542-6_31},
url = {https://mlanthology.org/eccv/2020/chen2020eccv-learning-e/}
}