10 citations · 52 across the 15 of their papers we have counts for
12 papers · 1 filter
Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration
Lanqing Guo, Zhiyuan Zha, Saiprasad Ravishankar +1
Constructing effective image priors is critical to solving ill-posed inverse problems in image processing and imaging. Recent works proposed to exploit image non-local similarity f…
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration
Zhiyuan Zha, Xin Yuan, Bihan Wen +3
In this paper, we propose a novel approach to the rank minimization problem, termed rank residual constraint (RRC) model. Different from existing low-rank based approaches, such as…
Group Sparsity Residual with Non-Local Samples for Image Denoising
Zhiyuan Zha, Xinggan Zhang, Qiong Wang +3
Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in G…
A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization
Zhiyuan Zha, Xin Yuan, Bihan Wen +3
Sparse coding has achieved a great success in various image processing tasks. However, a benchmark to measure the sparsity of image patch/group is missing since sparse coding is es…
Non-Convex Weighted Lp Nuclear Norm based ADMM Framework for Image Restoration
Zhiyuan Zha, Xinggan Zhang, Yu Wu +2
Since the matrix formed by nonlocal similar patches in a natural image is of low rank, the nuclear norm minimization (NNM) has been widely used in various image processing studies.…
Group-based Sparse Representation for Image Compressive Sensing Reconstruction with Non-Convex Regularization
Zhiyuan Zha, Xinggan Zhang, Qiong Wang +2
Patch-based sparse representation modeling has shown great potential in image compressive sensing (CS) reconstruction. However, this model usually suffers from some limits, such as…
Group Sparsity Residual Constraint for Image Denoising
Zhiyuan Zha, Xinggan Zhang, Qiong Wang +2
Group-based sparse representation has shown great potential in image denoising. However, most existing methods only consider the nonlocal self-similarity (NSS) prior of noisy input…
Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization based on Group Sparse Representation
Zhiyuan Zha, Xin Yuan, Bei Li +4
Rank minimization methods have attracted considerable interest in various areas, such as computer vision and machine learning. The most representative work is nuclear norm minimiza…
Image denoising using group sparsity residual and external nonlocal self-similarity prior
Zhiyuan Zha, Xinggan Zhang, Qiong Wang +2
Nonlocal image representation has been successfully used in many image-related inverse problems including denoising, deblurring and deblocking. However, a majority of reconstructio…
Analyzing the group sparsity based on the rank minimization methods
Zhiyuan Zha, Xin Liu, Xiaohua Huang +5
Sparse coding has achieved a great success in various image processing studies. However, there is not any benchmark to measure the sparsity of image patch/group because sparse disc…
Image denoising via group sparsity residual constraint
Zhiyuan Zha, Xin Liu, Ziheng Zhou +7
Group sparsity has shown great potential in various low-level vision tasks (e.g, image denoising, deblurring and inpainting). In this paper, we propose a new prior model for image…
A Comparative Study for the Nuclear Norms Minimization Methods
Zhiyuan Zha, Bihan Wen, Jiachao Zhang +2
The nuclear norm minimization (NNM) is commonly used to approximate the matrix rank by shrinking all singular values equally. However, the singular values have clear physical meani…