Zhiyuan Zha | DeepAI

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∙ 07/12/2021

R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery

State-of-the-art image denoisers exploit various types of deep neural ne...

0 Rongkai Zhang, et al. ∙

∙ 05/16/2020

The Power of Triply Complementary Priors for Image Compressive Sensing

Recent works that utilized deep models have achieved superior results in...

0 Zhiyuan Zha, et al. ∙

∙ 07/06/2018

From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Denoising

Inspired by the recent advances of Generative Adversarial Networks (GAN)...

2 Zhiyuan Zha, et al. ∙

∙ 03/22/2018

Group Sparsity Residual with Non-Local Samples for Image Denoising

Inspired by group-based sparse coding, recently proposed group sparsity ...

0 Zhiyuan Zha, et al. ∙

∙ 09/12/2017

Bridge the Gap Between Group Sparse Coding and Rank Minimization via Adaptive Dictionary Learning

Both sparse coding and rank minimization have led to great successes in ...

0 Zhiyuan Zha, et al. ∙

∙ 04/05/2017

Non-Convex Weighted Lp Minimization based Group Sparse Representation Framework for Image Denoising

Nonlocal image representation or group sparsity has attracted considerab...

0 Qiong Wang, et al. ∙

∙ 03/01/2017

Group Sparsity Residual Constraint for Image Denoising

Group-based sparse representation has shown great potential in image den...

0 Zhiyuan Zha, et al. ∙

∙ 01/03/2017

Image denoising using group sparsity residual and external nonlocal self-similarity prior

Nonlocal image representation has been successfully used in many image-r...

0 Zhiyuan Zha, et al. ∙

∙ 11/28/2016

Analyzing the group sparsity based on the rank minimization methods

Sparse coding has achieved a great success in various image processing s...

0 Zhiyuan Zha, et al. ∙

∙ 09/12/2016

Image denoising via group sparsity residual constraint

Group sparsity has shown great potential in various low-level vision tas...

0 Zhiyuan Zha, et al. ∙

∙ 08/16/2016

A Comparative Study for the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization

Nuclear norm minimization (NNM) tends to over-shrink the rank components...

0 Zhiyuan Zha, et al. ∙

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