10 citations · 21 across the 6 of their papers we have counts for
6 papers
Deep Probabilistic Unfolding for Quantized Compressive Sensing
Gang Qu, Ping Wang, Siming Zheng +1
We propose a deep probabilistic unfolding model to address the classical quantized compressive sensing problem that leverages an unfolding framework to enhance the reconstruction a…
Physics-guided Deep Unfolding Network for Enhanced Kronecker Compressive sensing
Gang Qu, Ping Wang, Siming Zheng +1
Deep networks have achieved remarkable success in image compressed sensing (CS) task, namely reconstructing a high-fidelity image from its compressed measurement. However, existing…
Unfolding Framework with Prior of Convolution-Transformer Mixture and Uncertainty Estimation for Video Snapshot Compressive Imaging
Siming Zheng, Xin Yuan
We consider the problem of video snapshot compressive imaging (SCI), where sequential high-speed frames are modulated by different masks and captured by a single measurement. The u…
Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms
Siming Zheng, Yujia Xue, Waleed Tahir +6
We consider the image and video compression on resource limited platforms. An ultra low-cost image encoder, named Block Modulating Video Compression (BMVC) with an encoding complex…
Two-Stage is Enough: A Concise Deep Unfolding Reconstruction Network for Flexible Video Compressive Sensing
Siming Zheng, Xiaoyu Yang, Xin Yuan
We consider the reconstruction problem of video compressive sensing (VCS) under the deep unfolding/rolling structure. Yet, we aim to build a flexible and concise model using minimu…
Deep Equilibrium Models for Video Snapshot Compressive Imaging
Yaping Zhao, Siming Zheng, Xin Yuan
The ability of snapshot compressive imaging (SCI) systems to efficiently capture high-dimensional (HD) data has led to an inverse problem, which consists of recovering the HD signa…