1 code implementation • 18 Jul 2023 • Bin Chen, Jiechong Song, Jingfen Xie, Jian Zhang
By absorbing the merits of both the model- and data-driven methods, deep physics-engaged learning scheme achieves high-accuracy and interpretable image reconstruction.
1 code implementation • 24 Apr 2022 • Jingfen Xie, Jian Zhang, Yongbing Zhang, Xiangyang Ji
Compressed Sensing MRI (CS-MRI) aims at reconstructing de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two basic issues, i. e., where to sample and how to reconstruct.
no code implementations • CVPR 2022 • Youmin Xu, Chong Mou, Yujie Hu, Jingfen Xie, Jian Zhang
Previous image steganography methods are limited in hiding capacity and robustness, commonly vulnerable to distortion on container images such as Gaussian noise, Poisson noise, and lossy compression.
1 code implementation • 15 Jul 2021 • Di You, Jian Zhang, Jingfen Xie, Bin Chen, Siwei Ma
In this paper, we propose a novel COntrollable Arbitrary-Sampling neTwork, dubbed COAST, to solve CS problems of arbitrary-sampling matrices (including unseen sampling matrices) with one single model.
1 code implementation • 22 Mar 2021 • Di You, Jingfen Xie, Jian Zhang
While deep neural networks have achieved impressive success in image compressive sensing (CS), most of them lack flexibility when dealing with multi-ratio tasks and multi-scene images in practical applications.