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Found 16 papers, 3 papers with code
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing
Furthermore, we present a unified framework for incorporating various GSR and LR models and discuss the relationship between GSR and LR models.
R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery
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
Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration
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.
The Power of Triply Complementary Priors for Image Compressive Sensing
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.
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration
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.
Group Sparsity Residual with Non-Local Samples for Image Denoising
Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing.
A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization
Sparse coding has achieved a great success in various image processing tasks.
Non-Convex Weighted Lp Nuclear Norm based ADMM Framework for Image Restoration
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
In this paper, a group-based sparse representation method with non-convex regularization (GSR-NCR) for image CS reconstruction is proposed.
Non-Convex Weighted Lp Minimization based Group Sparse Representation Framework for Image Denoising
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.
Group Sparsity Residual Constraint for Image Denoising
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.
Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization based on Group Sparse Representation
However, it still lacks a sound mathematical explanation on why WNNM is more feasible than NNM.
Image denoising using group sparsity residual and external nonlocal self-similarity prior
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.
Analyzing the group sparsity based on the rank minimization methods
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.
Image denoising via group sparsity residual constraint
Group sparsity has shown great potential in various low-level vision tasks (e. g, image denoising, deblurring and inpainting).
A Comparative Study for the Nuclear Norms Minimization Methods
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.
