BM3D Image Denoising Algorithm with Adaptive Distance Hard-threshold

Dai, Li; Zhang, You-Sai; Li, Yuanjiang

doi:10.14257/ijsip.2013.6.6.04

Cited by 14 publications

(7 citation statements)

References 5 publications

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“…The formula for matching criteria is in (2.5). For high value of noises, Euclidean distance has been proved to be not working well which leads to inaccurate block matching and in the literatures is called "block effect" [29]. Due to this issue, authors propose a pre-filtering hard thresholding step denoted by operator Υ of the coefficients for noise standard deviation greater than 40.…”

Section: Block Matching On Noisy Imagementioning

confidence: 99%

Image Denoising in Spatial and Transform Domains

Sharifymoghaddam¹

2021

Preprint

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Image denoising is an inseparable pre-processing step of many image processing algorithms. Two mostly used image denoising algorithms are Nonlocal Means (NLM) and Block Matching and 3D Transform Domain Collaborative Filtering (BM3D). While BM3D outperforms NLM on variety of natural images, NLM is usually preferred when the algorithm complexity is an issue. In this thesis, we suggest modified version of these two methods that improve the performance of the original approaches. The conventional NLM uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, it can include some dissimilar patches. Our first contribution, denoted by Similarity Validation Based Nonlocal Means (NLM-SVB), eliminates some of those unnecessary dissimilar patches in order to improve the performance of the algorithm. We propose a hard thresholding pre-processing step based on the exact distribution of distances of similar patches. Consequently, our method eliminates about 60% of dissimilar patches and improves NLM in terms of Peak Signal to Noise Ratio (PSNR) and Stracuteral Similarity Index Measure (SSIM). Our second contribution, denoted by Probabilistic Weighting BM3D (PW-BM3D), is the result of our thorough study of BM3D. BM3D consists of two main steps. One is finding a basic estimate of the noiseless image by hard thresholding coefficients. The second one is using this estimate to perform wiener filtering. In both steps the weighting scheme in the aggregation process plays an important role. The current weighting process depends on the variance of retrieved coefficients after denoising which results in a biased weighting. In PW-BM3D, we propose a novel probabilistic weighting scheme which is a function of the probability of similarity of noiseless patches in each 3D group. The results show improvement over BM3D in terms of PSNR for an average of about 0.2dB.

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Section: Block Matching On Noisy Imagementioning

confidence: 99%

Image Denoising in Spatial and Transform Domains

Sharifymoghaddam¹

2021

Preprint

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“…In Hard-thresholding stage, four-dimensional groups are formed by stacking together. The resemblance between two cubes is measured via the distance d [17].…”

Section: A Adaptive Hard Thresholding Stagementioning

confidence: 99%

“…Pierric and et al proposed a series of methods in [12] Magonni proposed an approach and later extended for varying variance estimation rather than fixed [4] [5].Recently Muhammad Aksam Iftikhar and et al [6] [8]proposed an extension to Non local Means for MRI and obtained promising results. Hosein M. Golshan and et al determined a method for MRI denoising using LLMSE [7].in [17]authors proposed an approach for modification of BM3D with adaptive threshold. The paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Hard Thresholding for Block Matching Three dimension for Denoising Volumetric Data

Kamalakshi¹

2018

IJRASET

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BM3D is a recent state of art patch based denoising algorithm .It works on the fact that an image has a locally sparse representation in transform domain.It is composed of two stages i)hard thresholding and ii)weiner filtering This paper provides a mechanism that incorporates an improved version of BM3D which combines the digital image characteristic with added noise pollution levels, and adaptively selects block-matching threshold in grouping stage for an extended BM3D to four dimension so as to denoise volumetric data corrupted by Gaussian and rician noise. Experimental results demonstrate it outperforms not only in terms of objective criteria of PSNR, but also in improving the visual quality.

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“…Zhang et al [27] proposed a method for calculating adaptive hard threshold used in d-distance of BM3D's first step . Here instead of using a single hard threshold, they adaptive generate the threshold value by using the gradient of the block, the SSIM between two blocks and the noise level.…”

Section: Extensions and Improvementsmentioning

confidence: 99%

BM3D Image Denoising using Learning-based Adaptive Hard Thresholding

Bashar¹,

El-Sakka

2016

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

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Image denoising is an important pre-processing step in most imaging applications. Block Matching and 3D Filtering (BM3D) is considered to be the current state-of-art algorithm for additive image denoising. But this algorithm uses a fixed hard thresholding scheme to attenuate noise from a 3D block. Experiments show that this fixed hard thresholding deteriorates the performance of BM3D because it does not consider the context of corresponding blocks. In this thesis, we propose a learning based adaptive hard thresholding method to solve this issue.Also, BM3D algorithm requires as an input the value of the noise level in the input image. But in real life it is not practical to pass as an input such noise level. In this thesis, we also attempt to automatically estimate the level of the noise in the input image. Experimental results demonstrate that our proposed algorithm outperforms BM3D in both objective and subjective fidelity criteria.

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BM3D Image Denoising Algorithm with Adaptive Distance Hard-threshold

Abstract: Abstract

Cited by 14 publications

References 5 publications

Image Denoising in Spatial and Transform Domains

Image Denoising in Spatial and Transform Domains

Adaptive Hard Thresholding for Block Matching Three dimension for Denoising Volumetric Data

BM3D Image Denoising using Learning-based Adaptive Hard Thresholding

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