Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function

2016 ◽  
Vol 6 (5) ◽  
pp. 2219
Author(s):  
Suhad A. Ali ◽  
C. Elaf A. Abbood ◽  
Shaymaa Abdu LKadhm
Keyword(s):  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Different Types ◽  
Estimation Function ◽  
Salt And Pepper ◽  
Gaussian Estimation

<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>

scholarly journals Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function

2016 ◽  
Vol 6 (5) ◽  
pp. 2219
Author(s):  
Suhad A. Ali ◽  
C. Elaf A. Abbood ◽  
Shaymaa Abdu LKadhm
Keyword(s):  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Different Types ◽  
Estimation Function ◽  
Salt And Pepper ◽  
Gaussian Estimation

<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>

scholarly journals An Improved Method to Remove Salt and Pepper Noise in Noisy Images

2021 ◽  
Author(s):  
Jinder Kaur ◽  
Gurwinder Kaur ◽  
Ashwani Kumar
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Similarity Index ◽  
Noise Removal ◽  
Mean Value ◽  
Salt And Pepper Noise ◽  
Gray Scale ◽  
Noisy Pixel ◽  
Rgb Images ◽  
Salt And Pepper

In the field of image processing, removal of noise from Gray scale as well as RGB images is an ambitious task. The important function of noise removal algorithm is to eliminate noise from a noisy image. The salt and pepper noise (SPN) is frequently arising into Gray scale and RGB images while capturing, acquiring and transmitting over the insecure several communication mechanisms. In past, the numerous noise removal methods have been introduced to extract the noise from images adulterated with SPN. The proposed work introduces the SPN removal algorithm for Gray scale at low along with high density noise (10\% to 90\%). According to the different conditions of proposed algorithm, the noisy pixel is reconstructed by Winsorized mean or mean value of all pixels except the centre pixel which are present in the processing window. The noise from an image can be removed by using the proposed algorithm without degrading the quality of image. The performance evaluation of proposed and modified decision based unsymmetric median filter (MDBUTMF) is done on the basis of different performance parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Image Enhancement Factor (IEF) and Structure Similarity Index Measurement (SSIM).

scholarly journals AN FPGA IMPLEMENTATION OF MODIFIED DECISION BASED UNSYMMETRICAL TRIMMED MEDIAN FILTER FOR THE REMOVAL OF SALT AND PEPPER NOISE IN DIGITAL IMAGES

2014 ◽  
pp. 159-164
Author(s):  
CHAITANYA BETHINA ◽  
M. PREMKUMAR
Keyword(s):  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Noise Removal ◽  
Mean Value ◽  
Low Noise ◽  
Color Images ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Salt And Pepper

A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. Images are often corrupted by impulse noise during acquisition and transmission; thus, an efficient noise suppression technique is required before subsequent image processing operations. Median filter (MF) is widely used in noise removal methods due to its denoising capability and computational efficiency. However, it is effective only for low noise densities. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than denoising techniques. Especially, the proposed method can preserve edges very well while removing salt and pepper noise. Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF) shows better results at low and medium noise densities. At high noise densities, their performance is poor. A new algorithm to remove high-density salt and pepper noise using modified Decision Based Unsymmetric Trimmed Median Filter (DBUTMF) is proposed. The proposed algorithm replaces the noisy pixel by trimmed median. Since our algorithm is algorithmically simple, it is very suitable to be applied to many real-time applications and higher noise densities. When all the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window. The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

scholarly journals Salt and Pepper Noise Removal Filter for 8-Bit Images Based on Local and Global Occurrences of Grey Levels as Selection Indicator

2015 ◽  
Vol 15 (2) ◽  
pp. 123-132 ◽  
Author(s):  
Isma Irum ◽  
Muhammad Sharif ◽  
Mudassar Raza ◽  
Mussarat Yasmin
Keyword(s):  
Noise Removal ◽  
Moving Window ◽  
Noisy Image ◽  
Salt And Pepper Noise ◽  
High Noise ◽  
Filtering Method ◽  
Salt And Pepper ◽  
True Values ◽  
Selection Of

A spatial resolution decision based trimmed moving window filtering method has been proposed for salt andpepper noise removal. It provides a criterion for the selection of neighboring pixels based on the probabilities of local and global occurrences of grey levels of noisy image. It works well for the high noise densities up to 90% and contributes very effectively in estimating the true or near to true values of the original image. In order to measure the quality of image, well known quantification measures SSIM and PSNR are used. The method is compared with other existing techniques and shows that it outperforms other methods in terms of PSNR.DOI: http://dx.doi.org/njst.v15i2.12128Nepal Journal of Science and Technology Vol. 15, No.2 (2014) 123-132

scholarly journals Elimination of Impulse Noise using Mean Median filter for Image Enhancement

2019 ◽  
pp. 278-288
Author(s):  
Trupti Arun Jangale ◽  
Raj Kumar Paul
Keyword(s):  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Human Perception ◽  
Experimental Result ◽  
Second Step ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
A New Technique ◽  
Salt And Pepper

In this method, we've got introduced a new technique for the improvement of gray scale images, when images are corrupted by salt and pepper noise that's additionally referred to as an impulse noise. Our suggested phenomena show a better output for Medium density impulse noise as compare to the opposite renowned filters like standard Median Filter (SMF), a decision based mostly Median Filter (DBMF) and modified decision based Median Filter (MDBMF), Nonlinear filter (NLF) and so on. Our projected technique worked on two steps, within the beginning is that the detection of noisy pixels and within the second step is that the removal of noisy pixels. For detection of noisy constituent apply condition pixels values lies in between 0 to 255 it noisy it's noisy free pixels. In our second step that's the removal of noisy pixel recommended technique that's replaces the noisy pixel by alpha trimmed mean median value. Different grayscale pictures are tested via proposed technique. The experimental result shows higher Peak Signal to Noise ratio (PSNR) values and with higher visual and human perception.

scholarly journals Probabilistic Decision Based Average Trimmed Filter for the Removal of High-Density Salt and Pepper Noise

2020 ◽  
Vol 8 (5) ◽  
pp. 4350-4357
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Absolute Error ◽  
Noise Removal ◽  
Low Noise ◽  
Salt And Pepper Noise ◽  
Ratio Image ◽  
Noise Density ◽  
Chest X Ray ◽  
Salt And Pepper

The paper focuses on the evacuation of salt and pepper noise from a contaminated image. A probabilistic decision based average trimmed filter (PDBATF) is proposed for both high and low noise density. The proposed algorithm addresses the issue related to even number of noise-free pixel in trimmed median filter for the calculation of processing pixel. The proposed average trimmed filter is incorporated for low noise density while the proposed patch else average trimmed filter is applied for high noise density. Finally, they are combined together to develop the proposed PDBATF. The proposed algorithm show an excellent noise removal capability compared to the recently developed algorithms in terms of peak signal to noise ratio, image enhancement factor, mean absolute error and execution time. It works very efficiently in de-noising contaminated medical images such as chest-x-ray and malaria-blood-smear.

scholarly journals An Iterative Weighted-Mean Filter for Removal of High-Density Salt-and-Pepper Noise

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1990
Author(s):  
Fengyu Chen ◽  
Minghui Huang ◽  
Zhuxi Ma ◽  
Yibo Li ◽  
Qianbin Huang
Keyword(s):  
Noise Removal ◽  
High Density ◽  
Salt And Pepper Noise ◽  
Weighted Mean ◽  
Real Time Processing ◽  
Noise Density ◽  
Mean Filter ◽  
Execution Speed ◽  
Salt And Pepper

Salt-and-pepper noise, which is often introduced by sharp and sudden disturbances in the image signal, greatly reduces the quality of images. Great progress has been made for the salt-and-pepper noise removal; however, the problem of image blur and distortion still exists, and the efficiency of denoising requires improvement. This paper proposes an iterative weighted-mean filter (IWMF) algorithm in detecting and removing high-density salt-and-pepper noise. Three steps are required to implement this algorithm: First, the noise value and distribution characteristics were used to identify the noise pixels, effectively improving the accuracy of noise detection. Second, a weighted-mean filter was applied to the noisy pixels. We adopted an un-fixed shape symmetrical window with better detail preservation ability. Third, this method was performed iteratively, avoiding the streak effect and artifacts in high noise density. The experimental results showed that IWMF outperformed other state-of-the-art filters at various noise densities, both in subjective visualization and objective digital measures. The extremely fast execution speed of this method is quite suitable for real-time processing.

scholarly journals Impulse Noise Removal Based on Hybrid Genetic Algorithm

2021 ◽  
Vol 38 (4) ◽  
pp. 1245-1251
Author(s):  
Nail Alaoui ◽  
Arwa Mashat ◽  
Amel Baha Houda Adamou-Mitiche ◽  
Lahcène Mitiche ◽  
Aicha Djalab ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Hybrid Genetic Algorithm ◽  
Structural Similarity ◽  
Noise Removal ◽  
Impulse Noise Removal ◽  
Salt And Pepper ◽  
Crossover And Mutation ◽  
Different Levels

In this paper, we introduce a new method, impulse noise removal based on hybrid genetic algorithm (INRHGA) to remove impulse noise at different noise densities of noise while preserving the main features of the image. The proposed approach merges the genetic algorithm and methods for filtering images that are combined into the population as essential solutions to create a developed and improved population. A set of individuals is developed into a number of iterations using factors of crossover and mutation. Our method develops a group of images instead of a set of parameters from the filters. We then introduced some of the concepts and steps of it. The proposed algorithm is compared with some image denoising algorithm. By using Peak Signal to Noise Ratio (PSNR), structural similarity (SSIM). For example, for Lenna image with 60% salt and pepper noise density, PSNR, SSIM results of AMF, MDBUTMFG and NAFSM methods are 20,39/ 28.74/ 29.85 and 0.5679/ 0.8312/ 0.8818 respectively, while PSNR, SSIM results of the proposed algorithm are 29.92 and 0.8838, respectively. Experimental results indicate that INRHGA is very effective and visually comparable with the above-mentioned methods at different levels of noise.

scholarly journals High Density Noise Reduction of Tea Leaves using Density Mass Filter (DMF)

2019 ◽  
Vol 8 (11) ◽  
pp. 2988-2991
Keyword(s):  
Gaussian Noise ◽  
Classification Accuracy ◽  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Salt And Pepper Noise ◽  
Tea Leaves ◽  
Signal To Noise ◽  
Mass Filter ◽  
Salt And Pepper

In agriculture digital image processing play an important role in the prediction of tea leaves diseases. But acquisition of image may be corrupted by various types of noise such as impulse noise, Gaussian noise and salt and pepper noise. These noises can corrupt the image. So it will reduce the quality of the image and it reduces the classification accuracy. Hence it needs a efficient filter to remove these noise. This paper introduced a new filter density mass filter. It reduces all kinds of noise. Two metrics PSNR (Peak Signal to Noise ratio) and RMSE (Root Mean Square Error) values are used to evaluate the quality of images. The PSNR value of proposed filter is significantly high and RMSE value is reasonably low

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