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

@article{Zha2017ImageDU,
  title={Image denoising using group sparsity residual and external nonlocal self-similarity prior},
  author={Zhiyuan Zha and Xinggan Zhang and Qiong Wang and Yechao Bai and Lan Tang},
  journal={2017 IEEE International Conference on Image Processing (ICIP)},
  year={2017},
  pages={2956-2960}
}
Nonlocal image representation has been successfully used in many image-related inverse problems including denoising, deblurring and deblocking. However, due to a majority of reconstruction methods only exploit the nonlocal self-similarity (NSS) prior of the degraded observation image, it is very challenging to reconstruct the latent clean image directly from the noisy observation. In this paper we propose a novel model for image denoising via group sparsity residual and external NSS prior. To… 

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References

SHOWING 1-10 OF 46 REFERENCES

Group Sparsity Residual Constraint for Image Denoising

Experimental results demonstrate that the proposed GSRC modeling outperforms many state-of-the-art denoising methods in terms of the objective and the perceptual metrics.

Image denoising via group sparsity residual constraint

A new prior model for image denoising via group sparsity residual constraint (GSRC) is proposed, and experimental results have demonstrated that the proposed method not only outperforms many state-of-the-art Denoising methods such as BM3D and WNNM, but results in a faster speed.

Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising

It is demonstrated that, owe to the learned PG-GMM, a simple weighted sparse coding model, which has a closed-form solution, can be used to perform image denoising effectively, resulting in high PSNR measure, fast speed, and particularly the best visual quality among all competing methods.

Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach

This paper takes a low-rank approach toward SSC and provides a conceptually simple interpretation from a bilateral variance estimation perspective, namely that singular-value decomposition of similar packed patches can be viewed as pooling both local and nonlocal information for estimating signal variances.

Nonlocally Centralized Sparse Representation for Image Restoration

The so-called nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, and the extensive experiments validate the generality and state-of-the-art performance of the proposed NCSR algorithm.

Image Restoration Using Joint Statistical Modeling in a Space-Transform Domain

A joint statistical modeling (JSM) in an adaptive hybrid space-transform domain is established, which offers a powerful mechanism of combining local smoothness and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation.

Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization

Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.

Image Restoration via Simultaneous Sparse Coding: Where Structured Sparsity Meets Gaussian Scale Mixture

A nonlocal extension of Gaussian scale mixture (GSM) model is developed using simultaneous sparse coding (SSC) and its applications into image restoration are explored and it is shown that the variances of sparse coefficients can be jointly estimated along with the unknown sparse coefficients via the method of alternating optimization.

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

This paper investigates the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image Denoising, and uses residual learning and batch normalization to speed up the training process as well as boost theDenoising performance.

Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

An algorithm based on an enhanced sparse representation in transform domain based on a specially developed collaborative Wiener filtering achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.