309 citations · 1.6k across the 77 of their papers we have counts for
7 papers · 1 filter
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction
Yuan Fang, Yipeng Liu, Jie Chen +4
In recent years, the fusion of high spatial resolution multispectral image (HR-MSI) and low spatial resolution hyperspectral image (LR-HSI) has been recognized as an effective meth…
Phase Guided Light Field for Spatial-Depth High Resolution 3D Imaging
Geyou Zhang, Ce Zhu, Kai Liu +1
On 3D imaging, light field cameras typically are of single shot, and however, they heavily suffer from low spatial resolution and depth accuracy. In this paper, by employing an opt…
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing
Zhiyuan Zha, Bihan Wen, Xin Yuan +3
The compressive sensing (CS) scheme exploits much fewer measurements than suggested by the Nyquist-Shannon sampling theorem to accurately reconstruct images, which has attracted co…
Real-World Single Image Super-Resolution: A Brief Review
Honggang Chen, Xiaohai He, Linbo Qing +3
Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) observation, has been an active research topic in the area o…
Hyperspectral Image Denoising with Partially Orthogonal Matrix Vector Tensor Factorization
Zhen Long, Yipeng Liu, Sixing Zeng +3
Hyperspectral image (HSI) has some advantages over natural image for various applications due to the extra spectral information. During the acquisition, it is often contaminated by…
The Power of Triply Complementary Priors for Image Compressive Sensing
Zhiyuan Zha, Xin Yuan, Joey Tianyi Zhou +3
Recent works that utilized deep models have achieved superior results in various image restoration applications. Such approach is typically supervised which requires a corpus of tr…
AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing
Zhonghao Zhang, Yipeng Liu, Jiani Liu +2
Most compressive sensing (CS) reconstruction methods can be divided into two categories, i.e. model-based methods and classical deep network methods. By unfolding the iterative opt…