Improved Adaptive Weighted Mean Filter for Salt-and-Pepper Noise Removal

Erkan, Uğur; Thanh, Dang N. H.; Enginoğlu, Serdar; Memiş, Samet

doi:10.1109/icecce49384.2020.9179351

Cited by 19 publications

(10 citation statements)

References 25 publications

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“…To indicate the denoising success of the proposed filter, we carried out an experimental study. The simulation results manifest that our DAMRmF outperforms AFMF (Erkan et al, 2020a), TVWA (Lu et al, 2016), UWMF (Kandemir et al, 2015), DAMF , AWMF (Zang & Li, 2014), ACmF (Enginoğlu et al, 2020), ARmF (Enginoğlu et al, 2019), and IAWMF (Erkan et al, 2020b) according to PSNR and SSIM (Wang et al, 2004) results for SPN densities varying from 60% to 90%. Besides, visual results herein validated the numerical results provided in the Simulation Results Subsection.…”

Section: Conclusion and Recommendationsmentioning

confidence: 75%

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Gri Tonlamalı Görüntülerdeki Yüksek Yoğunluklu Tuz ve Biber Gürültüsünü Kaldırmak için Farklı Uyarlamalı Modifiye Riesz Ortalama Filtresi

Memiş¹,

Erkan²

2021

European Journal of Science and Technology

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This paper proposes a new filter, Different Adaptive Modified Riesz Mean Filter (DAMRmF), for high-density salt-and-pepper noise (SPN) removal. DAMRmF operationalizes a pixel weight function and adaptivity condition of Adaptive Median Filter (AMF). In the simulation, the proposed filter is compared with Adaptive Frequency Median Filter (AFMF), Three-Values-Weighted Method (TVWM), Unbiased Weighted Mean Filter (UWMF), Different Applied Median Filter (DAMF), Adaptive Weighted Mean Filter (AWMF), Adaptive Cesáro Mean Filter (ACmF), Adaptive Riesz Mean Filter (ARmF), and Improved Adaptive Weighted Mean Filter (IAWMF) for 20 traditional test images with noise levels from 60% to 90%. The results show that DAMRmF outperforms the state-of-the-art filters in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) values. Moreover, DAMRmF also performs better than the state-of-the-art filters concerning mean PSNR and SSIM results. We finally discuss DAMRmF for further research.

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Section: Conclusion and Recommendationsmentioning

confidence: 75%

“…In this part of the study, we compare the proposed DAMRmF with AFMF (Erkan et al, 2020a), TVWA (Lu et al, 2016), UWMF (Kandemir et al, 2015), DAMF , AWMF (Zang & Li, 2014), ACmF (Enginoğlu et al, 2020), ARmF (Enginoğlu et al, 2019), and IAWMF (Erkan et al, 2020b) in terms of PSNR and SSIM (Wang et al, 2004) results.…”

Section: Experimental Studymentioning

confidence: 99%

Gri Tonlamalı Görüntülerdeki Yüksek Yoğunluklu Tuz ve Biber Gürültüsünü Kaldırmak için Farklı Uyarlamalı Modifiye Riesz Ortalama Filtresi

Memiş¹,

Erkan²

2021

European Journal of Science and Technology

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“…Common methods for removing salt and pepper noise include median-filter-based methods [7][8][9][10][11][12][13][14][15][16], total variation (TV)-based methods [17][18][19][20][21], Euler's elastica variational method [22], deep-learning-based methods [23][24][25], and wavelet transform (WT)-based methods [26]. Median-filter-based methods replace the noise pixel with the median value of adjacent pixels.…”

Section: Introductionmentioning

confidence: 99%

Salt and pepper noise removal method based on stationary Framelet transform with non‐convex sparsity regularization

Chen

Huang

Wang

et al. 2022

IET Image Processing

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Salt and pepper noise occurs randomly and causes image degradation. Numerous denoising methods have been proposed to suppress this noise. However, existing methods have two main limitations. First, noise characteristics, such as noise location information and sparsity, are often described inaccurately or even ignored. Second, many existing methods separate the contaminated image into a recovered image and a noise part, leading to the recovery of an image with unsatisfactory smooth and detailed parts. In this study, the authors introduce a noise detection strategy to determine the position of the noise and employ a non-convex sparsity regularization depicted by l p quasi-norm to describe the sparsity of the noise, thereby addressing the first limitation. We adopt the morphological component analysis framework with stationary Framelet transform to decompose the processed image into the cartoon, texture, and noise parts to resolve the second limitation. Then, the proposed model is applied by using the alternating direction method of multipliers (ADMM). Finally, experiments are conducted to verify the proposed method and compare it with some current state-of-the-art denoising methods. The experimental results show that the proposed method can remove salt and pepper noise while preserving the details of the processed image and outperforming some state-of-the-art methods.

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“…SPN denoising is a popular field of study. There are many studies in SPN denoising ( Loya & Keskar, 2015 ; Erkan et al, 2020a ; Chen, Hung & Zou, 2017 ; Wang et al, 2016b ; Erkan et al, 2019 ; Wang et al, 2016a ; Zhang & Li, 2014 ; Erkan et al, 2020b ; Thanh et al, 2020a ; Enginoğlu, Erkan & Memiş, 2020 ; Memis & Erkan, 2021 ; Enginoğlu, Erkan & Memiş, 2019 ; Thanh et al, 2020b ; Yildiz & Yildiz, 2018 ; Gökcen & Kalyoncu, 2020 ; Erkan & Gökrem, 2018 ; Duan & Zhang, 2010 ; Erkan, Gökrem & Enginoğlu, 2018 ; Erkan & Gökrem, 2017 ). The SPN is cleaned with spatial filters in the image.…”

Section: Introductionmentioning

confidence: 99%

“…The SPN is cleaned with spatial filters in the image. Average, weighted average and other averaging filters have better results when the noisy pixel values are calculated via the average of the pixel values without noise ( Wang et al, 2016b ; Erkan et al, 2019 ; Wang et al, 2016a ; Zhang & Li, 2014 ; Erkan et al, 2020b ; Thanh et al, 2020a ; Enginoğlu, Erkan & Memiş, 2020 ; Memis & Erkan, 2021 ; Enginoğlu, Erkan & Memiş, 2019 ; Xu & Aminu, 2022 ; Hien et al, 2022 , Bindal & Garg, 2022 ; Aslam et al, 2022 ). Due to the linearity of these filters, blurring occurs while improving the image.…”

Section: Introductionmentioning

confidence: 99%

A new approach for SPN removal: nearest value based mean filter

Turan¹

2022

PeerJ Computer Science

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In this study, a new adaptive filter is proposed to eliminate salt and pepper noise (SPN). The basis of the proposed method consists of two-stages. (1) Changing the noisy pixel value with the closest pixel value or assigning their average to the noisy pixel in case there is more than one pixel with the same distance; (2) the updating of the calculated noisy pixel values with the average filter by correlating them with the noise ratio. The method developed was named as Nearest Value Based Mean Filter (NVBMF), because of using the pixel value which the closest distance in the first stage. Results obtained with the proposed method: it has been compared with the results obtained with the Adaptive Frequency Median Filter, Adaptive Riesz Mean Filter, Improved Adaptive Weighted Mean Filter, Adaptive Switching Weight Mean Filter, Adaptive Weighted Mean Filter, Different Applied Median Filter, Iterative Mean Filter, Two-Stage Filter, Multistage Selective Convolution Filter, Different Adaptive Modified Riesz Mean Filter, Stationary Framelet Transform Based Filter and A New Type Adaptive Median Filter methods. In the comparison phase, nine different noise levels were applied to the original images. Denoised images were compared using Peak Signal-to-Noise Ratio, Image Enhancement Factor, and Structural Similarity Index Map image quality metrics. Comparisons were made using three separate image datasets and Cameraman, Airplane images. NVBMF achieved the best result in 52 out of 84 comparisons for PSNR, best in 47 out of 84 comparisons for SSIM, and best in 36 out of 84 comparisons for IEF. In addition, values nearly to the best result were obtained in comparisons where the best result could not be reached. The results obtained show that the NVBMF can be used as an effective method in denoising SPN.

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Improved Adaptive Weighted Mean Filter for Salt-and-Pepper Noise Removal

Cited by 19 publications

References 25 publications

Gri Tonlamalı Görüntülerdeki Yüksek Yoğunluklu Tuz ve Biber Gürültüsünü Kaldırmak için Farklı Uyarlamalı Modifiye Riesz Ortalama Filtresi

Gri Tonlamalı Görüntülerdeki Yüksek Yoğunluklu Tuz ve Biber Gürültüsünü Kaldırmak için Farklı Uyarlamalı Modifiye Riesz Ortalama Filtresi

Salt and pepper noise removal method based on stationary Framelet transform with non‐convex sparsity regularization

A new approach for SPN removal: nearest value based mean filter

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