1 code implementation • 17 Mar 2022 • Zhiyuan Zha, Bihan Wen, Xin Yuan, Saiprasad Ravishankar, Jiantao Zhou, Ce Zhu
Furthermore, we present a unified framework for incorporating various GSR and LR models and discuss the relationship between GSR and LR models.
no code implementations • 12 Jul 2021 • Rongkai Zhang, Jiang Zhu, Zhiyuan Zha, Justin Dauwels, Bihan Wen
To benchmark the effectiveness of reinforcement learning in R3L, we train a recurrent neural network with the same architecture for residual recovery using the deterministic loss, thus to analyze how the two different training strategies affect the denoising performance.
Ranked #1 on Image Denoising on BSD68 sigma30
1 code implementation • 24 Jun 2020 • Lanqing Guo, Zhiyuan Zha, Saiprasad Ravishankar, Bihan Wen
Experimental results demonstrate that (1) Self-Convolution can significantly speed up most of the popular non-local image restoration algorithms, with two-fold to nine-fold faster block matching, and (2) the proposed multi-modality image restoration scheme achieves superior denoising results in both efficiency and effectiveness on RGB-NIR images.
no code implementations • 16 May 2020 • Zhiyuan Zha, Xin Yuan, Joey Tianyi Zhou, Jiantao Zhou, Bihan Wen, Ce Zhu
In this paper, we propose a joint low-rank and deep (LRD) image model, which contains a pair of triply complementary priors, namely \textit{external} and \textit{internal}, \textit{deep} and \textit{shallow}, and \textit{local} and \textit{non-local} priors.
1 code implementation • 6 Jul 2018 • Zhiyuan Zha, Xin Yuan, Bihan Wen, Jiantao Zhou, Jiachao Zhang, Ce Zhu
Towards this end, we first obtain a good reference of the original image groups by using the image NSS prior, and then the rank residual of the image groups between this reference and the degraded image is minimized to achieve a better estimate to the desired image.
no code implementations • 22 Mar 2018 • Zhiyuan Zha, Xinggan Zhang, Qiong Wang, Yechao Bai, Lan Tang, Xin Yuan
Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing.
no code implementations • 12 Sep 2017 • Zhiyuan Zha, Xin Yuan, Bihan Wen, Jiantao Zhou, Jiachao Zhang, Ce Zhu
Sparse coding has achieved a great success in various image processing tasks.
no code implementations • 24 Apr 2017 • Zhiyuan Zha, Xinggan Zhang, Qiong Wang, Lan Tang, Xin Liu
In this paper, a group-based sparse representation method with non-convex regularization (GSR-NCR) for image CS reconstruction is proposed.
no code implementations • 24 Apr 2017 • Zhiyuan Zha, Xinggan Zhang, Yu Wu, Qiong Wang, Lan Tang
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.
no code implementations • 5 Apr 2017 • Qiong Wang, Xinggan Zhang, Yu Wu, Lan Tang, Zhiyuan Zha
Nonlocal image representation or group sparsity has attracted considerable interest in various low-level vision tasks and has led to several state-of-the-art image denoising techniques, such as BM3D, LSSC.
no code implementations • 1 Mar 2017 • Zhiyuan Zha, Xinggan Zhang, Qiong Wang, Lan Tang, Xin Liu
Unlike the conventional group-based sparse representation denoising methods, two kinds of prior, namely, the NSS priors of noisy and pre-filtered images, are used in GSRC.
no code implementations • 15 Feb 2017 • Zhiyuan Zha, Xin Yuan, Bei Li, Xinggan Zhang, Xin Liu, Lan Tang, Ying-Chang Liang
However, it still lacks a sound mathematical explanation on why WNNM is more feasible than NNM.
no code implementations • 3 Jan 2017 • Zhiyuan Zha, Xinggan Zhang, Qiong Wang, Yechao Bai, Lan Tang
To boost the performance of image denoising, the concept of group sparsity residual is proposed, and thus the problem of image denoising is transformed into one that reduces the group sparsity residual.
no code implementations • 28 Nov 2016 • Zhiyuan Zha, Xin Liu, Xiaohua Huang, Henglin Shi, Yingyue Xu, Qiong Wang, Lan Tang, Xinggan Zhang
Then, we prove that group-based sparse coding is equivalent to the rank minimization problem, and thus the sparse coefficient of each group is measured by estimating the singular values of each group.
no code implementations • 12 Sep 2016 • Zhiyuan Zha, Xin Liu, Ziheng Zhou, Xiaohua Huang, Jingang Shi, Zhenhong Shang, Lan Tang, Yechao Bai, Qiong Wang, Xinggan Zhang
Group sparsity has shown great potential in various low-level vision tasks (e. g, image denoising, deblurring and inpainting).
no code implementations • 16 Aug 2016 • Zhiyuan Zha, Bihan Wen, Jiachao Zhang, Jiantao Zhou, Ce Zhu
Inspired by enhancing sparsity of the weighted L1-norm minimization in comparison with L1-norm minimization in sparse representation, we thus explain that WNNM is more effective than NMM.