An Improved Image Filtering Algorithm for Mixed Noise

In recent years, image filtering has been a hot research direction in the field of image processing. Experts and scholars have proposed many methods for noise removal in images, and these methods have achieved quite good denoising results. However, most methods are performed on single noise, such as...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Chun He, Ke Guo, Huayue Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	salt and pepper noise gaussian noise median filter wavelet threshold denoising nonlocal means Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/452d914f9ad5443fa5e17cf91f1125af
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:452d914f9ad5443fa5e17cf91f1125af
record_format	dspace
spelling	oai:doaj.org-article:452d914f9ad5443fa5e17cf91f1125af2021-11-11T15:23:44ZAn Improved Image Filtering Algorithm for Mixed Noise10.3390/app1121103582076-3417https://doaj.org/article/452d914f9ad5443fa5e17cf91f1125af2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10358https://doaj.org/toc/2076-3417In recent years, image filtering has been a hot research direction in the field of image processing. Experts and scholars have proposed many methods for noise removal in images, and these methods have achieved quite good denoising results. However, most methods are performed on single noise, such as Gaussian noise, salt and pepper noise, multiplicative noise, and so on. For mixed noise removal, such as salt and pepper noise + Gaussian noise, although some methods are currently available, the denoising effect is not ideal, and there are still many places worthy of improvement and promotion. To solve this problem, this paper proposes a filtering algorithm for mixed noise with salt and pepper + Gaussian noise that combines an improved median filtering algorithm, an improved wavelet threshold denoising algorithm and an improved Non-local Means (NLM) algorithm. The algorithm makes full use of the advantages of the median filter in removing salt and pepper noise and demonstrates the good performance of the wavelet threshold denoising algorithm and NLM algorithm in filtering Gaussian noise. At first, we made improvements to the three algorithms individually, and then combined them according to a certain process to obtain a new method for removing mixed noise. Specifically, we adjusted the size of window of the median filtering algorithm and improved the method of detecting noise points. We improved the threshold function of the wavelet threshold algorithm, analyzed its relevant mathematical characteristics, and finally gave an adaptive threshold. For the NLM algorithm, we improved its Euclidean distance function and the corresponding distance weight function. In order to test the denoising effect of this method, salt and pepper + Gaussian noise with different noise levels were added to the test images, and several state-of-the-art denoising algorithms were selected to compare with our algorithm, including K-Singular Value Decomposition (KSVD), Non-locally Centralized Sparse Representation (NCSR), Structured Overcomplete Sparsifying Transform Model with Block Cosparsity (OCTOBOS), Trilateral Weighted Sparse Coding (TWSC), Block Matching and 3D Filtering (BM3D), and Weighted Nuclear Norm Minimization (WNNM). Experimental results show that our proposed algorithm is about 2–7 dB higher than the above algorithms in Peak Signal-Noise Ratio (PSNR), and also has better performance in Root Mean Square Error (RMSE), Structural Similarity (SSIM), and Feature Similarity (FSIM). In general, our algorithm has better denoising performance, better restoration of image details and edge information, and stronger robustness than the above-mentioned algorithms.Chun HeKe GuoHuayue ChenMDPI AGarticlesalt and pepper noisegaussian noisemedian filterwavelet threshold denoisingnonlocal meansTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10358, p 10358 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	salt and pepper noise gaussian noise median filter wavelet threshold denoising nonlocal means Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	salt and pepper noise gaussian noise median filter wavelet threshold denoising nonlocal means Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Chun He Ke Guo Huayue Chen An Improved Image Filtering Algorithm for Mixed Noise
description	In recent years, image filtering has been a hot research direction in the field of image processing. Experts and scholars have proposed many methods for noise removal in images, and these methods have achieved quite good denoising results. However, most methods are performed on single noise, such as Gaussian noise, salt and pepper noise, multiplicative noise, and so on. For mixed noise removal, such as salt and pepper noise + Gaussian noise, although some methods are currently available, the denoising effect is not ideal, and there are still many places worthy of improvement and promotion. To solve this problem, this paper proposes a filtering algorithm for mixed noise with salt and pepper + Gaussian noise that combines an improved median filtering algorithm, an improved wavelet threshold denoising algorithm and an improved Non-local Means (NLM) algorithm. The algorithm makes full use of the advantages of the median filter in removing salt and pepper noise and demonstrates the good performance of the wavelet threshold denoising algorithm and NLM algorithm in filtering Gaussian noise. At first, we made improvements to the three algorithms individually, and then combined them according to a certain process to obtain a new method for removing mixed noise. Specifically, we adjusted the size of window of the median filtering algorithm and improved the method of detecting noise points. We improved the threshold function of the wavelet threshold algorithm, analyzed its relevant mathematical characteristics, and finally gave an adaptive threshold. For the NLM algorithm, we improved its Euclidean distance function and the corresponding distance weight function. In order to test the denoising effect of this method, salt and pepper + Gaussian noise with different noise levels were added to the test images, and several state-of-the-art denoising algorithms were selected to compare with our algorithm, including K-Singular Value Decomposition (KSVD), Non-locally Centralized Sparse Representation (NCSR), Structured Overcomplete Sparsifying Transform Model with Block Cosparsity (OCTOBOS), Trilateral Weighted Sparse Coding (TWSC), Block Matching and 3D Filtering (BM3D), and Weighted Nuclear Norm Minimization (WNNM). Experimental results show that our proposed algorithm is about 2–7 dB higher than the above algorithms in Peak Signal-Noise Ratio (PSNR), and also has better performance in Root Mean Square Error (RMSE), Structural Similarity (SSIM), and Feature Similarity (FSIM). In general, our algorithm has better denoising performance, better restoration of image details and edge information, and stronger robustness than the above-mentioned algorithms.
format	article
author	Chun He Ke Guo Huayue Chen
author_facet	Chun He Ke Guo Huayue Chen
author_sort	Chun He
title	An Improved Image Filtering Algorithm for Mixed Noise
title_short	An Improved Image Filtering Algorithm for Mixed Noise
title_full	An Improved Image Filtering Algorithm for Mixed Noise
title_fullStr	An Improved Image Filtering Algorithm for Mixed Noise
title_full_unstemmed	An Improved Image Filtering Algorithm for Mixed Noise
title_sort	improved image filtering algorithm for mixed noise
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/452d914f9ad5443fa5e17cf91f1125af
work_keys_str_mv	AT chunhe animprovedimagefilteringalgorithmformixednoise AT keguo animprovedimagefilteringalgorithmformixednoise AT huayuechen animprovedimagefilteringalgorithmformixednoise AT chunhe improvedimagefilteringalgorithmformixednoise AT keguo improvedimagefilteringalgorithmformixednoise AT huayuechen improvedimagefilteringalgorithmformixednoise
_version_	1718435362577055744

An Improved Image Filtering Algorithm for Mixed Noise

Ejemplares similares