Choice of the Parameter for BM3D Denoising Algorithm Using No- Reference Metric

Mamaev, Nikolay; Yurin, D. V.; Krylov, Andrey S.

doi:10.1109/euvip.2018.8611679

Cited by 10 publications

(4 citation statements)

References 19 publications

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“…We use the algorithm described in (Mamaev et al, 2018) to find the optimal parameters for image denoising. Its idea is based on the assumption that noise and structures are uncorrelated, and that a perfect image denoising algorithm removes only noise while keeping structures intact so the difference between noisy and restored images contains only noise.…”

Section: Automatic Parameter Choicementioning

confidence: 99%

Non-Linear Multi-Frame Image Denoising Using Weighted Nuclear Norm Minimization

Nasonov

Volodina

Krylov

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. We address the problem of constructing single low noise image from a sequence of multiple noisy images. We use the approach based on finding and averaging similar blocks in the image and extend it to multiple images. Unlike traditional multi-frame super-resolution algorithms, the block-matching approach does not require computationally expensive motion estimation for multi-frame image denoising. In this work, we use an algorithm based on weighted nuclear minimization for image denoising. The evaluation of the algorithm shows noticeable improvement of image quality when using multiple input images instead of single one. The improvement is the most noticeable in the areas with complex non-repeated structure.

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Section: Automatic Parameter Choicementioning

confidence: 99%

Non-Linear Multi-Frame Image Denoising Using Weighted Nuclear Norm Minimization

Nasonov

Volodina

Krylov

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…We use the algorithm [5] for non-reference parameter choice. The algorithm is based on the assumption that the difference between input noisy and denoised images should not have features belonging to original image.…”

Section: Noreference Parameter Choicementioning

confidence: 99%

“…For example, the work [3] analyzes the edge characteristics, the work [4] calculates image statistics for speckle noise reduction. In [5], a analysis of the contents in the difference image between the original noisy image and the processed image is performed. Its idea comes from an assumption, that in the ideal case the difference image must contain just random values without any structures from the original image.…”

Section: Introductionmentioning

confidence: 99%

Automatic Choice of Denoising Parameter in Perona-Malik Model

Насонов

Nasonov

Мамаев

et al. 2019

GraphiCon'2019 Proceedings. Volume 2

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In this work, we propose a no-reference method for automatic choice of the parameters of Perona-Malik image diffusion algorithm for the problem of image denoising. The idea of the approach it to analyze and quantify the presence of structures in the difference image between the noisy image and the processed image as the mutual information value. We apply the proposed method to photographic images and to retinal images with modeled Gaussian noise with different parameters and analyze the effects of no-reference parameter choice compared to the optimal results. The proposed algorithm shows the effectiveness of no-reference parameter choice for the problem of image denoising.

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“…Therefore image detection has become one of the important topics with theoretical significance and practical application [8][9][10][11][12]. The main objective of image detection is to remove the noise while retaining the maximum amount of useful information in the image [13][14][15][16]. For different noise types, different detection algorithms are used, and the classical denoising algorithms are mainly mean filtering, Gaussian filtering, Wiener filtering, etc.…”

Section: Introductionmentioning

confidence: 99%

BM3D denoising-based multi-target detection method for complex background radar images

2023

Applied Mathematics and Nonlinear Sciences

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For the complex background of traditional radar image target detection methods is affected by multiple interferers, which leads to a low recognition rate of multi-target classification and a high false alarm rate. In this paper, the current hot research BM3D denoising method is introduced into radar image target detection. Firstly, the detection process of the BM3D algorithm is constructed, and the image pixels are reasonably clustered by denoising so that the image blocks in homogeneous regions have a high matching degree and those in non-homogeneous regions have a low matching degree. Secondly, the clustering recognition feature is used to limit the search range of similar blocks to homogeneous regions, to improve the denoising efficiency of the algorithm. Finally, the simulation results of this paper show that compared with previous algorithms, the BM3D detection method has significantly improved in both the subjective visual effect map and objective numerical indexes. For example, the image denoising quality is improved by about 0.716dB on average and up to 1.031dB, the image denoising time is shortened by about 19.962s on average, and the algorithm runs 1.211 times faster than the original algorithm. It is proved that the algorithm can improve denoising efficiency and reduce computational complexity while enhancing detailed information.

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Choice of the Parameter for BM3D Denoising Algorithm Using No- Reference Metric

Cited by 10 publications

References 19 publications

Non-Linear Multi-Frame Image Denoising Using Weighted Nuclear Norm Minimization

Non-Linear Multi-Frame Image Denoising Using Weighted Nuclear Norm Minimization

Automatic Choice of Denoising Parameter in Perona-Malik Model

BM3D denoising-based multi-target detection method for complex background radar images

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