2 citations · 3 across the 5 of their papers we have counts for
5 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…
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction Networks for Single-Pixel Imaging
Ping Wang, Lishun Wang, Gang Qu +3
Deep-unrolling and plug-and-play (PnP) approaches have become the de-facto standard solvers for single-pixel imaging (SPI) inverse problem. PnP approaches, a class of iterative alg…
Dual-Scale Transformer for Large-Scale Single-Pixel Imaging
Gang Qu, Ping Wang, Xin Yuan
Single-pixel imaging (SPI) is a potential computational imaging technique which produces image by solving an illposed reconstruction problem from few measurements captured by a sin…
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…