Improved Non-Local Means Algorithm Based on Dimensionality Reduction

Maruf, Golam Morshed; El-Sakka, Mahmoud R.

doi:10.1007/978-3-319-20801-5_5

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…The image neighborhoods are projects to a lower dimension space using PCA and the reduced subspace is used for computing similarities. A similar dimension reduction approach has also been proposed by Maruf and El-Sakka (Maruf and El-Sakka, 2015), where the image neighborhood are projected to a lower dimension by using t-test.…”

Section: Introductionmentioning

confidence: 99%

Non-local Means using Adaptive Weight Thresholding

Khan

El-Sakka

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Non-local means (NLM) is a popular image denoising scheme for reducing additive Gaus-sian noise. It uses a patch-based approach to find similar regions within a search neighborhood and estimate the denoised pixel based on the weighted average of all the pixels in the neighborhood. All the pixels are considered for averaging, irrespective of the value of their weights. This thesis proposes an improved variant of the original NLM scheme, called Weight Thresh-olded Non-Local Means (WTNLM), by thresholding the weights of the pixels within the search neighborhood, where the thresholded weights are used in the averaging step. The key parameters of the WTNLM are defined using learning-based models. In addition, the proposed method is used as a two-step approach for image denoising. At the first step, WTNLM is applied to generate a basic estimate of the denoised image. The second step applies WTNLM once more but with different smoothing strength. Experiments show that the denoising performance of the proposed method is better than that of the original NLM scheme, and its variants. It also outperforms the state-of-the-art image denoising scheme, BM3D, but only at low noise levels (σ ≤ 80).

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Section: Introductionmentioning

confidence: 99%

Non-local Means using Adaptive Weight Thresholding

Khan

El-Sakka

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Adaptive Non-local Means Using Weight Thresholding

Khan

El-Sakka

2017

Communications in Computer and Information Science

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Non-local means (NLM) is a popular image denoising scheme for reducing additive Gaussian noise. It uses a patch-based approach to find similar regions within a search neighborhood and estimate the denoised pixel based on the weighted average of all the pixels in the neighborhood. All the pixels are considered for averaging, irrespective of the value of their weights.This thesis proposes an improved variant of the original NLM scheme, called Weight Thresholded Non-Local Means (WTNLM), by thresholding the weights of the pixels within the search neighborhood, where the thresholded weights are used in the averaging step. The key parameters of the WTNLM are defined using learning-based models. In addition, the proposed method is used as a two-step approach for image denoising. At the first step, WTNLM is applied to generate a basic estimate of the denoised image. The second step applies WTNLM once more but with different smoothing strength. Experiments show that the denoising performance of the proposed method is better than that of the original NLM scheme, and its variants. It also outperforms the state-of-the-art image denoising scheme, BM3D, but only at low noise levels (σ ≤ 80).

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Modified Difference Squared Image Based Non Local Means Filter

Mehmood

Kaur

2020

2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

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Improved Non-Local Means Algorithm Based on Dimensionality Reduction

Cited by 3 publications

References 14 publications

Non-local Means using Adaptive Weight Thresholding

Non-local Means using Adaptive Weight Thresholding

Adaptive Non-local Means Using Weight Thresholding

Modified Difference Squared Image Based Non Local Means Filter

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