Mixed noise removal by weighted low rank model

Jiang, Jielin; Yang, Jian; Cui, Yan; Luo, Lei

doi:10.1016/j.neucom.2014.10.017

Cited by 26 publications

(42 citation statements)

References 32 publications

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“…The ratio of the SPIN varies from 10% to 60% with the step length of 10%. The proposed algorithm is compared with different methods for mixed noise removal as TF [22], SBF [23], MNF [24], Cai [27], AMF coupled with LRA [28], AMF coupled with LRR [29], WLRA [30], and WLRR [30]. We firstly compare the proposed algorithm with several classical methods as TF [22], SBF [23], and MNF [24].…”

Section: Resultsmentioning

confidence: 99%

“…In order to further verify the performance of the proposed algorithm, the proposed algorithm is compared with some existing main methods as Cai [27], AMF coupled with LRA [28], AMF coupled with LRR [29], WLRA [30], and WLRR [30]. Tables 1 and 2 present the denoising results (PSNR) of different methods for six test images with =50% and =60%, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, the low rank approximation (LRA) [28] and low rank representation (LRR) [29] have been used for removing noise. A weighted low rank approximation (WLRA) model and a weighted low rank representation (WLRR) model [30] are proposed to remove the mixed noise, which group image nonlocal similar patches as a matrix and restore the corrupted image by finding the weighted low rank approximation or representation of the matrix.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mixed Noise Removal Algorithm Combining Adaptive Directional Weighted Mean Filter and Improved Adaptive Anisotropic Diffusion Model

Nie

2018

Mathematical Problems in Engineering

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A mixed noise removal algorithm combining adaptive directional weighted mean filter and improved adaptive anisotropic diffusion model is proposed. Firstly, a noise classification method is introduced to divide all pixels into two types as the pixels corrupted by impulse noise and the pixels corrupted by Gaussian noise. Then an adaptive directional weighted mean filter is developed to remove impulse noise, which can adaptively select the optimal direction template from twelve direction templates and replace the gray level of each impulse noise corrupted pixel by the weighted mean gray level of pixels on the optimal direction template. Finally, an improved adaptive anisotropic diffusion model is developed to remove Gaussian noise in the initial denoised image, which can finely classify image features as smooth regions, edges, corners, and isolated noises by characteristic parameters and variance parameter and conduct adaptive diffusion for different image features by designing reasonable eigenvalues of diffusion tensor. A large number of experimental results show that the proposed algorithm outperforms many existing main mixed noise removal methods in terms of image denoising and detail preservation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mixed Noise Removal Algorithm Combining Adaptive Directional Weighted Mean Filter and Improved Adaptive Anisotropic Diffusion Model

Nie

2018

Mathematical Problems in Engineering

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“…Vidal et al suggested to use robust principal component analysis (RPCA) [32] to remove noise for LRR [15]. Furthermore, Jiang et al combined RPCA and RLRR together and developed a more robust version of LRR [33].…”

Section: Introductionmentioning

confidence: 99%

Spectral clustering steered low-rank representation for subspace segmentation

Wei

Yin

2016

Journal of Visual Communication and Image Representation

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Low-rank representation (LRR)and its variations have achieved great successes in subspace segmentation tasks. However, the segmentation processes of the existing LRR-related methods are all divided into two separated steps: affinity graphs construction and segmentation results obtainment. In the second step, normalize cut (Ncut) algorithm is used to get the final results based on the constructed graphs. This implies that the affinity graphs obtained by LRR-related algorithms may not be most suitable for Ncut, and the best results are not guaranteed to be achieved. In this paper, we propose a spectral clustering steered LRR representation algorithm (SCSLRR) which combines the objection functions of Ncut, K-means and LRR together. By solving a joint optimization problem, SCSLRR is able to find low-rank affinity matrices which are most beneficial for Ncut to get best segmentation results. The extensive experiments of subspace segmentation on several benchmark datasets show that SCSLRR dominates the related methods.

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“…Jiang et al [30] adopted the weighted encoding technique to remove Gaussian noise and impulse noise jointly. Subsequently, Jiang et al [31] presented a novel mixed noise removal method by proposing a weighted low rank model, where the image global structure and local edges can be well preserved via the low rank model fitting. However, these mixed noise removal models are only suited to dot noise, and need to use the classical approaches of Gaussian noise removal to finish the denoising task.…”

Section: Introductionmentioning

confidence: 99%