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Download View publication Copy reference Copy caption Embed figure Mean PSNR and SSIM of several algorithms for the 15 traditional images with different SPN ratios.

Source publication Different applied median filter in salt and pepper noise Article Full-text available Aug 2018 Uur Erkan Levent Gkrem Serdar Enginolu In this paper, we proposed a new method, Different Applied Median Filter (DAMF), to remove salt and pepper (SAP) noise at all densities.

Afterwards, we compared the results of DAMF method and some other methods by using Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) for some images suc.

Cite Download full-text Similar publications 1 Non-local mean filtering algorithm based on deep learning Article Full-text available Jan 2018 Baozhong Liu Jianbin Liu Aimed at the problem that the traditional image denoising algorithm is not effective in noise reduction, a new image denoising method is proposed.

The method combines deep learning and non-local mean filtering algorithms to denoise the noisy image to obtain better noise reduction effect.

By comparing with Gaussian filtering algorithm, median filter.

View Robustness of Median Filter For Suppression of Salt and Pepper Noise (SPN) and Random Valued Impulse Noise (RVIN) Article Full-text available Apr 2018 Abdul Rasak Adegoke Zubair Hammed Busari Noises in images are caused by many sources.

Results of numerical experiments on the robustness of median filter for suppression of Salt and Pepper Noise (SPN) and Random Valued Impulse Noise (RVIN) of varying noise densities are presented and discussed.

View Adaptive trimmed mean autoregressive model for reduction of poisson noise in scintigraphic images Article Full-text available Dec 2018 Dr KB Khattak A 2-D Adaptive Trimmed Mean Autoregressive (ATMAR) model has been proposed for denoising of medical images corrupted with Poisson noise.

Unfiltered images are divided into smaller chunks and ATMAR model is applied on each chunk separately.

In this paper, two 5x5 windows with 40 overlap are used to predict the center pixel value of the central row.

View 3 Edge-aware image denoising algorithm Article Full-text available Oct 2018 Xiangning Zhang Yan Yang Lening Lin The key of image denoising algorithms is to preserve the details of the original image while denoising the noise in the image.

The existing algorithms use the external information to better preserve the details of the image, but the use of external information needs the support of similar images or image patches.

View ADAPTIVE TRIMMED MEAN AUTOREGRESSIVE MODEL FOR REDUCTION OF POISSON NOISE IN SCINTIGRAPHIC IMAGES Article Full-text available Dec 2018 Khan Bahadar Khan Muhammad Shahid Hayat Ullah.

Muhammad Mohsin Khan A 2-D Adaptive Trimmed Mean Autoregressive (ATMAR) model has been proposed for denoising of medical images corrupted with poisson noise.

In this paper, two 5x5 windows with 40 overlapping are used to predict the center pixel value of the central.

Using median filter, the processing pixel of the image replaces with median value of the processing and neighboring pixels...

Vijaykumar et al.

Faragallah and Ibrahem 23 to suppress SP noise.

Erkan et al. 24 have suggested a new method called Different Applied Median Filter (DAMF) to remove SP noise at all densities.

Pixel value close to the original pixel is obtained using the value of neighbor pixels and adaptive window instead of fixed window size in the method..

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Analog circuit implementation based on median filter for salt and pepper noise reduction in image Article Full-text available Apr 2021 Melih Yildirim In this study, an analog circuit design instead of using a software is utilized to remove Salt and Pepper (SP) noise in image.

For this purpose, a digital image filtering method which is called median filter is carried out.

In median filter method, a current-mode nine-input one-output circuit architecture is preferred since 3 3 filtering mask is employed.

Median filter circuit is based on a modified bitonic sort network and consists of 26 maxmin selector circuits.